{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Report 2\n",
    "    ·张子龙\n",
    "    ·2018300053\n",
    "## 1. 问题：Titanic\n",
    "### 1.1 任务描述：\n",
    "    泰坦尼克号沉没事件是历史上最臭名昭著的沉船事故之一。1912年4月15日，泰坦尼克号在首次航行中与冰山相撞后沉没，2224名乘客和机组人员中1502人死亡。这一耸人听闻的悲剧震惊了国际社会，并导致对船舶实施更好的安全条例。\n",
    "\n",
    "    沉船事故造成这种生命损失的原因之一是没有足够的救生艇供乘客和船员使用。虽然在沉船事故中幸存下来有一些运气因素，但一些群体比其他人更有可能幸存下来，如妇女、儿童和上层阶级。\n",
    "\n",
    "    在这个挑战中，我们要求你完成对什么样的人可能生存的分析。我们特别要求您应用机器学习工具来预测哪些乘客在这场悲剧中幸存下来。\n",
    "### 1.2 练习技能：\n",
    "    · 二进制分类\n",
    "    · python和sklearn\n",
    "### 1.3 数据介绍："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|变量|类型|含义|\n",
    "|---|---|---|\n",
    "|PassengerId|整数|乘客编号|\n",
    "|Survived|整数|表示乘客是否生还，0 表示死亡，1 表示生还，test.csv中没有这一部分|\n",
    "|Pclass|整数|票级，是社会经济地位的一种代表。1、2、3 分别表示上层、中层和低层|\n",
    "|Name|字符串|乘客姓名|\n",
    "|Sex|字符串|性别|\n",
    "|Age|浮点数|年龄|\n",
    "|Sibsp|整数|在船上的兄弟姐妹的数量|\n",
    "|Parch|整数|在船上的父母子女的数量|\n",
    "|Ticket|字符串|票号|\n",
    "|Fare|浮点数|票价|\n",
    "|Cabin|字符串|（轮船上生活或睡觉的）隔间|\n",
    "|Embarked|字符串|登船口|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 解题思路\n",
    "    \n",
    "###  数据的整体分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "from pandas import Series,DataFrame\n",
    "train_data = pd.read_csv(\"train.csv\")\n",
    "test_data = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "861          862         0       2   \n",
       "862          863         1       1   \n",
       "863          864         0       3   \n",
       "864          865         0       2   \n",
       "865          866         1       2   \n",
       "866          867         1       2   \n",
       "867          868         0       1   \n",
       "868          869         0       3   \n",
       "869          870         1       3   \n",
       "870          871         0       3   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "873          874         0       3   \n",
       "874          875         1       2   \n",
       "875          876         1       3   \n",
       "876          877         0       3   \n",
       "877          878         0       3   \n",
       "878          879         0       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                     Moran, Mr. James    male   NaN      0   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                       Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                      Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                         Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                                Rice, Master. Eugene    male   2.0      4   \n",
       "17                        Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                             Masselmani, Mrs. Fatima  female   NaN      0   \n",
       "20                                Fynney, Mr. Joseph J    male  35.0      0   \n",
       "21                               Beesley, Mr. Lawrence    male  34.0      0   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female  15.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "24                       Palsson, Miss. Torborg Danira  female   8.0      3   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0      1   \n",
       "26                             Emir, Mr. Farred Chehab    male   NaN      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female   NaN      0   \n",
       "29                                 Todoroff, Mr. Lalio    male   NaN      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "861                        Giles, Mr. Frederick Edward    male  21.0      1   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.0      0   \n",
       "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female   NaN      8   \n",
       "864                             Gill, Mr. John William    male  24.0      0   \n",
       "865                           Bystrom, Mrs. (Karolina)  female  42.0      0   \n",
       "866                       Duran y More, Miss. Asuncion  female  27.0      1   \n",
       "867               Roebling, Mr. Washington Augustus II    male  31.0      0   \n",
       "868                        van Melkebeke, Mr. Philemon    male   NaN      0   \n",
       "869                    Johnson, Master. Harold Theodor    male   4.0      1   \n",
       "870                                  Balkic, Mr. Cerin    male  26.0      0   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n",
       "872                           Carlsson, Mr. Frans Olof    male  33.0      0   \n",
       "873                        Vander Cruyssen, Mr. Victor    male  47.0      0   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n",
       "876                      Gustafsson, Mr. Alfred Ossian    male  20.0      0   \n",
       "877                               Petroff, Mr. Nedelio    male  19.0      0   \n",
       "878                                 Laleff, Mr. Kristo    male   NaN      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "881                                 Markun, Mr. Johann    male  33.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "883                      Banfield, Mr. Frederick James    male  28.0      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket      Fare        Cabin Embarked  \n",
       "0        0         A/5 21171    7.2500          NaN        S  \n",
       "1        0          PC 17599   71.2833          C85        C  \n",
       "2        0  STON/O2. 3101282    7.9250          NaN        S  \n",
       "3        0            113803   53.1000         C123        S  \n",
       "4        0            373450    8.0500          NaN        S  \n",
       "5        0            330877    8.4583          NaN        Q  \n",
       "6        0             17463   51.8625          E46        S  \n",
       "7        1            349909   21.0750          NaN        S  \n",
       "8        2            347742   11.1333          NaN        S  \n",
       "9        0            237736   30.0708          NaN        C  \n",
       "10       1           PP 9549   16.7000           G6        S  \n",
       "11       0            113783   26.5500         C103        S  \n",
       "12       0         A/5. 2151    8.0500          NaN        S  \n",
       "13       5            347082   31.2750          NaN        S  \n",
       "14       0            350406    7.8542          NaN        S  \n",
       "15       0            248706   16.0000          NaN        S  \n",
       "16       1            382652   29.1250          NaN        Q  \n",
       "17       0            244373   13.0000          NaN        S  \n",
       "18       0            345763   18.0000          NaN        S  \n",
       "19       0              2649    7.2250          NaN        C  \n",
       "20       0            239865   26.0000          NaN        S  \n",
       "21       0            248698   13.0000          D56        S  \n",
       "22       0            330923    8.0292          NaN        Q  \n",
       "23       0            113788   35.5000           A6        S  \n",
       "24       1            349909   21.0750          NaN        S  \n",
       "25       5            347077   31.3875          NaN        S  \n",
       "26       0              2631    7.2250          NaN        C  \n",
       "27       2             19950  263.0000  C23 C25 C27        S  \n",
       "28       0            330959    7.8792          NaN        Q  \n",
       "29       0            349216    7.8958          NaN        S  \n",
       "..     ...               ...       ...          ...      ...  \n",
       "861      0             28134   11.5000          NaN        S  \n",
       "862      0             17466   25.9292          D17        S  \n",
       "863      2          CA. 2343   69.5500          NaN        S  \n",
       "864      0            233866   13.0000          NaN        S  \n",
       "865      0            236852   13.0000          NaN        S  \n",
       "866      0     SC/PARIS 2149   13.8583          NaN        C  \n",
       "867      0          PC 17590   50.4958          A24        S  \n",
       "868      0            345777    9.5000          NaN        S  \n",
       "869      1            347742   11.1333          NaN        S  \n",
       "870      0            349248    7.8958          NaN        S  \n",
       "871      1             11751   52.5542          D35        S  \n",
       "872      0               695    5.0000  B51 B53 B55        S  \n",
       "873      0            345765    9.0000          NaN        S  \n",
       "874      0         P/PP 3381   24.0000          NaN        C  \n",
       "875      0              2667    7.2250          NaN        C  \n",
       "876      0              7534    9.8458          NaN        S  \n",
       "877      0            349212    7.8958          NaN        S  \n",
       "878      0            349217    7.8958          NaN        S  \n",
       "879      1             11767   83.1583          C50        C  \n",
       "880      1            230433   26.0000          NaN        S  \n",
       "881      0            349257    7.8958          NaN        S  \n",
       "882      0              7552   10.5167          NaN        S  \n",
       "883      0  C.A./SOTON 34068   10.5000          NaN        S  \n",
       "884      0   SOTON/OQ 392076    7.0500          NaN        S  \n",
       "885      5            382652   29.1250          NaN        Q  \n",
       "886      0            211536   13.0000          NaN        S  \n",
       "887      0            112053   30.0000          B42        S  \n",
       "888      2        W./C. 6607   23.4500          NaN        S  \n",
       "889      0            111369   30.0000         C148        C  \n",
       "890      0            370376    7.7500          NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "      <td>Kelly, Mr. James</td>\n",
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       "      <td>895</td>\n",
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       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>315154</td>\n",
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>12.2875</td>\n",
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       "      <td>897</td>\n",
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       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
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       "      <td>30.0</td>\n",
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       "      <td>330972</td>\n",
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       "      <td>899</td>\n",
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       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
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       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
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       "      <td>Ilieff, Mr. Ylio</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>11</th>\n",
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       "      <td>Jones, Mr. Charles Cresson</td>\n",
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       "      <td>46.0</td>\n",
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       "      <td>694</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>905</td>\n",
       "      <td>2</td>\n",
       "      <td>Howard, Mr. Benjamin</td>\n",
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       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24065</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>906</td>\n",
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       "      <td>Chaffee, Mrs. Herbert Fuller (Carrie Constance...</td>\n",
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       "      <td>47.0</td>\n",
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       "      <td>W.E.P. 5734</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
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       "      <td>del Carlo, Mrs. Sebastiano (Argenia Genovesi)</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>908</td>\n",
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       "      <td>Keane, Mr. Daniel</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>233734</td>\n",
       "      <td>12.3500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>909</td>\n",
       "      <td>3</td>\n",
       "      <td>Assaf, Mr. Gerios</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2692</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>910</td>\n",
       "      <td>3</td>\n",
       "      <td>Ilmakangas, Miss. Ida Livija</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101270</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>911</td>\n",
       "      <td>3</td>\n",
       "      <td>Assaf Khalil, Mrs. Mariana (Miriam\")\"</td>\n",
       "      <td>female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2696</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>912</td>\n",
       "      <td>1</td>\n",
       "      <td>Rothschild, Mr. Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>55.0</td>\n",
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       "      <td>PC 17603</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>913</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Master. Artur Karl</td>\n",
       "      <td>male</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>C 17368</td>\n",
       "      <td>3.1708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>914</td>\n",
       "      <td>1</td>\n",
       "      <td>Flegenheim, Mrs. Alfred (Antoinette)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17598</td>\n",
       "      <td>31.6833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>915</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Richard Norris II</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.3792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>916</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Mrs. Arthur Larned (Emily Maria Borie)</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>917</td>\n",
       "      <td>3</td>\n",
       "      <td>Robins, Mr. Alexander A</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 3337</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>918</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Miss. Helene Ragnhild</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B36</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>919</td>\n",
       "      <td>3</td>\n",
       "      <td>Daher, Mr. Shedid</td>\n",
       "      <td>male</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2698</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>920</td>\n",
       "      <td>1</td>\n",
       "      <td>Brady, Mr. John Bertram</td>\n",
       "      <td>male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113054</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>A21</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>921</td>\n",
       "      <td>3</td>\n",
       "      <td>Samaan, Mr. Elias</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>1280</td>\n",
       "      <td>3</td>\n",
       "      <td>Canavan, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364858</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1281</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Paul Folke</td>\n",
       "      <td>male</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>1282</td>\n",
       "      <td>1</td>\n",
       "      <td>Payne, Mr. Vivian Ponsonby</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12749</td>\n",
       "      <td>93.5000</td>\n",
       "      <td>B24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1283</td>\n",
       "      <td>1</td>\n",
       "      <td>Lines, Mrs. Ernest H (Elizabeth Lindsey James)</td>\n",
       "      <td>female</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17592</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>D28</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>1284</td>\n",
       "      <td>3</td>\n",
       "      <td>Abbott, Master. Eugene Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 2673</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1285</td>\n",
       "      <td>2</td>\n",
       "      <td>Gilbert, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 30769</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>1286</td>\n",
       "      <td>3</td>\n",
       "      <td>Kink-Heilmann, Mr. Anton</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>315153</td>\n",
       "      <td>22.0250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1287</td>\n",
       "      <td>1</td>\n",
       "      <td>Smith, Mrs. Lucien Philip (Mary Eloise Hughes)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13695</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>C31</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1288</td>\n",
       "      <td>3</td>\n",
       "      <td>Colbert, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>371109</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1289</td>\n",
       "      <td>1</td>\n",
       "      <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13567</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>B41</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1290</td>\n",
       "      <td>3</td>\n",
       "      <td>Larsson-Rondberg, Mr. Edvard A</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347065</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1291</td>\n",
       "      <td>3</td>\n",
       "      <td>Conlon, Mr. Thomas Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21332</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1292</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Caroline</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36928</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>C7</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>1293</td>\n",
       "      <td>2</td>\n",
       "      <td>Gale, Mr. Harry</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28664</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1294</td>\n",
       "      <td>1</td>\n",
       "      <td>Gibson, Miss. Dorothy Winifred</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112378</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1295</td>\n",
       "      <td>1</td>\n",
       "      <td>Carrau, Mr. Jose Pedro</td>\n",
       "      <td>male</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113059</td>\n",
       "      <td>47.1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1296</td>\n",
       "      <td>1</td>\n",
       "      <td>Frauenthal, Mr. Isaac Gerald</td>\n",
       "      <td>male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17765</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>D40</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>1297</td>\n",
       "      <td>2</td>\n",
       "      <td>Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2166</td>\n",
       "      <td>13.8625</td>\n",
       "      <td>D38</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>1298</td>\n",
       "      <td>2</td>\n",
       "      <td>Ware, Mr. William Jeffery</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28666</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1299</td>\n",
       "      <td>1</td>\n",
       "      <td>Widener, Mr. George Dunton</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113503</td>\n",
       "      <td>211.5000</td>\n",
       "      <td>C80</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1300</td>\n",
       "      <td>3</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>334915</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1301</td>\n",
       "      <td>3</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "      <td>female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SOTON/O.Q. 3101315</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1302</td>\n",
       "      <td>3</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365237</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1304</td>\n",
       "      <td>3</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347086</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                               Name  \\\n",
       "0            892       3                                   Kelly, Mr. James   \n",
       "1            893       3                   Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                          Myles, Mr. Thomas Francis   \n",
       "3            895       3                                   Wirz, Mr. Albert   \n",
       "4            896       3       Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "5            897       3                         Svensson, Mr. Johan Cervin   \n",
       "6            898       3                               Connolly, Miss. Kate   \n",
       "7            899       2                       Caldwell, Mr. Albert Francis   \n",
       "8            900       3          Abrahim, Mrs. Joseph (Sophie Halaut Easu)   \n",
       "9            901       3                            Davies, Mr. John Samuel   \n",
       "10           902       3                                   Ilieff, Mr. Ylio   \n",
       "11           903       1                         Jones, Mr. Charles Cresson   \n",
       "12           904       1      Snyder, Mrs. John Pillsbury (Nelle Stevenson)   \n",
       "13           905       2                               Howard, Mr. Benjamin   \n",
       "14           906       1  Chaffee, Mrs. Herbert Fuller (Carrie Constance...   \n",
       "15           907       2      del Carlo, Mrs. Sebastiano (Argenia Genovesi)   \n",
       "16           908       2                                  Keane, Mr. Daniel   \n",
       "17           909       3                                  Assaf, Mr. Gerios   \n",
       "18           910       3                       Ilmakangas, Miss. Ida Livija   \n",
       "19           911       3              Assaf Khalil, Mrs. Mariana (Miriam\")\"   \n",
       "20           912       1                             Rothschild, Mr. Martin   \n",
       "21           913       3                          Olsen, Master. Artur Karl   \n",
       "22           914       1               Flegenheim, Mrs. Alfred (Antoinette)   \n",
       "23           915       1                    Williams, Mr. Richard Norris II   \n",
       "24           916       1    Ryerson, Mrs. Arthur Larned (Emily Maria Borie)   \n",
       "25           917       3                            Robins, Mr. Alexander A   \n",
       "26           918       1                       Ostby, Miss. Helene Ragnhild   \n",
       "27           919       3                                  Daher, Mr. Shedid   \n",
       "28           920       1                            Brady, Mr. John Bertram   \n",
       "29           921       3                                  Samaan, Mr. Elias   \n",
       "..           ...     ...                                                ...   \n",
       "388         1280       3                               Canavan, Mr. Patrick   \n",
       "389         1281       3                        Palsson, Master. Paul Folke   \n",
       "390         1282       1                         Payne, Mr. Vivian Ponsonby   \n",
       "391         1283       1     Lines, Mrs. Ernest H (Elizabeth Lindsey James)   \n",
       "392         1284       3                      Abbott, Master. Eugene Joseph   \n",
       "393         1285       2                               Gilbert, Mr. William   \n",
       "394         1286       3                           Kink-Heilmann, Mr. Anton   \n",
       "395         1287       1     Smith, Mrs. Lucien Philip (Mary Eloise Hughes)   \n",
       "396         1288       3                               Colbert, Mr. Patrick   \n",
       "397         1289       1  Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...   \n",
       "398         1290       3                     Larsson-Rondberg, Mr. Edvard A   \n",
       "399         1291       3                           Conlon, Mr. Thomas Henry   \n",
       "400         1292       1                            Bonnell, Miss. Caroline   \n",
       "401         1293       2                                    Gale, Mr. Harry   \n",
       "402         1294       1                     Gibson, Miss. Dorothy Winifred   \n",
       "403         1295       1                             Carrau, Mr. Jose Pedro   \n",
       "404         1296       1                       Frauenthal, Mr. Isaac Gerald   \n",
       "405         1297       2       Nourney, Mr. Alfred (Baron von Drachstedt\")\"   \n",
       "406         1298       2                          Ware, Mr. William Jeffery   \n",
       "407         1299       1                         Widener, Mr. George Dunton   \n",
       "408         1300       3                    Riordan, Miss. Johanna Hannah\"\"   \n",
       "409         1301       3                          Peacock, Miss. Treasteall   \n",
       "410         1302       3                             Naughton, Miss. Hannah   \n",
       "411         1303       1    Minahan, Mrs. William Edward (Lillian E Thorpe)   \n",
       "412         1304       3                     Henriksson, Miss. Jenny Lovisa   \n",
       "413         1305       3                                 Spector, Mr. Woolf   \n",
       "414         1306       1                       Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                       Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                                Ware, Mr. Frederick   \n",
       "417         1309       3                           Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare  \\\n",
       "0      male  34.5      0      0              330911    7.8292   \n",
       "1    female  47.0      1      0              363272    7.0000   \n",
       "2      male  62.0      0      0              240276    9.6875   \n",
       "3      male  27.0      0      0              315154    8.6625   \n",
       "4    female  22.0      1      1             3101298   12.2875   \n",
       "5      male  14.0      0      0                7538    9.2250   \n",
       "6    female  30.0      0      0              330972    7.6292   \n",
       "7      male  26.0      1      1              248738   29.0000   \n",
       "8    female  18.0      0      0                2657    7.2292   \n",
       "9      male  21.0      2      0           A/4 48871   24.1500   \n",
       "10     male   NaN      0      0              349220    7.8958   \n",
       "11     male  46.0      0      0                 694   26.0000   \n",
       "12   female  23.0      1      0               21228   82.2667   \n",
       "13     male  63.0      1      0               24065   26.0000   \n",
       "14   female  47.0      1      0         W.E.P. 5734   61.1750   \n",
       "15   female  24.0      1      0       SC/PARIS 2167   27.7208   \n",
       "16     male  35.0      0      0              233734   12.3500   \n",
       "17     male  21.0      0      0                2692    7.2250   \n",
       "18   female  27.0      1      0    STON/O2. 3101270    7.9250   \n",
       "19   female  45.0      0      0                2696    7.2250   \n",
       "20     male  55.0      1      0            PC 17603   59.4000   \n",
       "21     male   9.0      0      1             C 17368    3.1708   \n",
       "22   female   NaN      0      0            PC 17598   31.6833   \n",
       "23     male  21.0      0      1            PC 17597   61.3792   \n",
       "24   female  48.0      1      3            PC 17608  262.3750   \n",
       "25     male  50.0      1      0           A/5. 3337   14.5000   \n",
       "26   female  22.0      0      1              113509   61.9792   \n",
       "27     male  22.5      0      0                2698    7.2250   \n",
       "28     male  41.0      0      0              113054   30.5000   \n",
       "29     male   NaN      2      0                2662   21.6792   \n",
       "..      ...   ...    ...    ...                 ...       ...   \n",
       "388    male  21.0      0      0              364858    7.7500   \n",
       "389    male   6.0      3      1              349909   21.0750   \n",
       "390    male  23.0      0      0               12749   93.5000   \n",
       "391  female  51.0      0      1            PC 17592   39.4000   \n",
       "392    male  13.0      0      2           C.A. 2673   20.2500   \n",
       "393    male  47.0      0      0          C.A. 30769   10.5000   \n",
       "394    male  29.0      3      1              315153   22.0250   \n",
       "395  female  18.0      1      0               13695   60.0000   \n",
       "396    male  24.0      0      0              371109    7.2500   \n",
       "397  female  48.0      1      1               13567   79.2000   \n",
       "398    male  22.0      0      0              347065    7.7750   \n",
       "399    male  31.0      0      0               21332    7.7333   \n",
       "400  female  30.0      0      0               36928  164.8667   \n",
       "401    male  38.0      1      0               28664   21.0000   \n",
       "402  female  22.0      0      1              112378   59.4000   \n",
       "403    male  17.0      0      0              113059   47.1000   \n",
       "404    male  43.0      1      0               17765   27.7208   \n",
       "405    male  20.0      0      0       SC/PARIS 2166   13.8625   \n",
       "406    male  23.0      1      0               28666   10.5000   \n",
       "407    male  50.0      1      1              113503  211.5000   \n",
       "408  female   NaN      0      0              334915    7.7208   \n",
       "409  female   3.0      1      1  SOTON/O.Q. 3101315   13.7750   \n",
       "410  female   NaN      0      0              365237    7.7500   \n",
       "411  female  37.0      1      0               19928   90.0000   \n",
       "412  female  28.0      0      0              347086    7.7750   \n",
       "413    male   NaN      0      0           A.5. 3236    8.0500   \n",
       "414  female  39.0      0      0            PC 17758  108.9000   \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   \n",
       "416    male   NaN      0      0              359309    8.0500   \n",
       "417    male   NaN      1      1                2668   22.3583   \n",
       "\n",
       "               Cabin Embarked  \n",
       "0                NaN        Q  \n",
       "1                NaN        S  \n",
       "2                NaN        Q  \n",
       "3                NaN        S  \n",
       "4                NaN        S  \n",
       "5                NaN        S  \n",
       "6                NaN        Q  \n",
       "7                NaN        S  \n",
       "8                NaN        C  \n",
       "9                NaN        S  \n",
       "10               NaN        S  \n",
       "11               NaN        S  \n",
       "12               B45        S  \n",
       "13               NaN        S  \n",
       "14               E31        S  \n",
       "15               NaN        C  \n",
       "16               NaN        Q  \n",
       "17               NaN        C  \n",
       "18               NaN        S  \n",
       "19               NaN        C  \n",
       "20               NaN        C  \n",
       "21               NaN        S  \n",
       "22               NaN        S  \n",
       "23               NaN        C  \n",
       "24   B57 B59 B63 B66        C  \n",
       "25               NaN        S  \n",
       "26               B36        C  \n",
       "27               NaN        C  \n",
       "28               A21        S  \n",
       "29               NaN        C  \n",
       "..               ...      ...  \n",
       "388              NaN        Q  \n",
       "389              NaN        S  \n",
       "390              B24        S  \n",
       "391              D28        S  \n",
       "392              NaN        S  \n",
       "393              NaN        S  \n",
       "394              NaN        S  \n",
       "395              C31        S  \n",
       "396              NaN        Q  \n",
       "397              B41        C  \n",
       "398              NaN        S  \n",
       "399              NaN        Q  \n",
       "400               C7        S  \n",
       "401              NaN        S  \n",
       "402              NaN        C  \n",
       "403              NaN        S  \n",
       "404              D40        C  \n",
       "405              D38        C  \n",
       "406              NaN        S  \n",
       "407              C80        C  \n",
       "408              NaN        Q  \n",
       "409              NaN        S  \n",
       "410              NaN        Q  \n",
       "411              C78        Q  \n",
       "412              NaN        S  \n",
       "413              NaN        S  \n",
       "414             C105        C  \n",
       "415              NaN        S  \n",
       "416              NaN        S  \n",
       "417              NaN        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后我们对数据的主要信息进行查看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将数据这样表示出来之后很容易发现数据是有缺少的部分的。\n",
    "然后我们来看看数字类数据的大致情况如何："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面这张表中我们又可以得出：只有38.3838%的乘客在这次灾难中存活了下来，船上人的年龄均值大致为29岁。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Sagesser, Mlle. Emma</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name   Sex    Ticket        Cabin Embarked\n",
       "count                    891   891       891          204      889\n",
       "unique                   891     2       681          147        3\n",
       "top     Sagesser, Mlle. Emma  male  CA. 2343  C23 C25 C27        S\n",
       "freq                       1   577         7            4      644"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe(include=[np.object])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 再对各个特征单独分析\n",
    "然后我们看看座舱等级的分布情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "train_data.Pclass.value_counts().plot(kind = 'bar')\n",
    "plt.title(u\"Ticket class\")\n",
    "plt.ylabel(u\"people\")\n",
    "plt.xlabel(u\"Ticket class\")\n",
    "plt.grid(True)\n",
    "plt.show()#将座舱等级用图表示出来，更为直观"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按正常情况来说，等级应该是3->2->1越来越高的。\n",
    "然后我们再看看每个阶层的生还率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算各阶层的生还率\n",
    "train_data[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很显而易见的就是，生还率是随着阶级的提高而提高的。阶级最高的那部分人生还率有62%左右，但是最底层的人民却只有24%左右的生还率，几乎就是最高阶层的三分之一。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后我们再来看看不同性别对于存活率的影响："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived\n",
       "0  0.742038\n",
       "1  0.188908"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#各性别获救比例计算\n",
    "train_data[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean()[[\"Survived\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们很容易发现，女性生存下来的比率是远远高于男性的。对于这种结果发生的原因，我在网上也查找了有关资料，其中有一点我觉得分析的很有道理，虽然很残酷：在这种情况下，人的本能是远高于他的礼貌的。如有兴趣可以点击后方链接了解：\n",
    "[一种对泰坦尼克号为何女性存活率远高于男性的分析](https://baijiahao.baidu.com/s?id=1671612885059825087&wfr=spider&for=pc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再对年龄进行分析以及可视化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived = train_data['Age'][train_data.Survived == 1].plot(kind = 'kde',legend = True,label = \"survived\",figsize=(9,5))\n",
    "notsurvived = train_data['Age'][train_data.Survived == 0].plot(kind = 'kde',legend= True,label = \"not survived\",grid = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图又可以发现，孩子们的生存概率又是偏大的。所以我们就可以认为，小孩在这种情况下是优先级较高的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将Fare也进行可视化的操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived = train_data['Fare'][train_data.Survived == 1].plot(kind = 'kde',legend = True,label = \"survived\",figsize=(9,5))\n",
    "notsurvived = train_data['Fare'][train_data.Survived == 0].plot(kind = 'kde',legend= True,label = \"not survived\",grid = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，船票越贵，存活率越高，这与我们最开始对船舱等级的分析基本吻合。\n",
    "\n",
    "不同口的生存率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Embarked  Survived\n",
      "0        C  0.553571\n",
      "1        Q  0.389610\n",
      "2        S  0.336957\n"
     ]
    }
   ],
   "source": [
    "print(train_data[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "容易看出c口登船的人的生存率要高很多。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据处理\n",
    "    在最开始我们就把训练集中的数据给列出来了，很容易发现是有数据缺失的。那我们先看看，哪些数据是缺失的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，cabin，age和embarked都是有数据的缺失的。那我们改如何处理这几类数据呢？cabin对我们而言，在实际生活中就是没有什么意义的，所以我们将这部分的数据舍弃掉。对age和emarked，我们用平均值来进行填充。其他的无关数据我们也进行删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除Ticket信息\n",
    "train_data.drop(\"Ticket\", axis=1, inplace=True)  \n",
    "test_data.drop(\"Ticket\", axis=1, inplace=True)  \n",
    "# 去除Cabin信息\n",
    "train_data.drop(\"Cabin\", axis=1, inplace=True)\n",
    "test_data.drop(\"Cabin\", axis=1, inplace=True)\n",
    "# 去除Name信息\n",
    "train_data.drop(\"Name\", axis=1, inplace=True)\n",
    "test_data.drop(\"Name\", axis=1, inplace=True)\n",
    "# 去除PassengerId信息\n",
    "train_data.drop(\"PassengerId\", axis=1, inplace=True)\n",
    "test_data.drop(\"PassengerId\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Age'].fillna(train_data['Age'].mean(), inplace=True)  \n",
    "test_data['Age'].fillna(test_data['Age'].mean(), inplace=True)  # 用平均值填充缺失\n",
    "test_data['Fare'].fillna(test_data['Fare'].mean(), inplace=True)  # 用平均值填充空白"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 替换字符串\n",
    "train_data['Sex'].replace('female', 0.0, inplace=True)\n",
    "test_data['Sex'].replace('female', 0.0, inplace=True)\n",
    "\n",
    "train_data['Sex'].replace('male', 1.0, inplace=True)\n",
    "test_data['Sex'].replace('male', 1.0, inplace=True)\n",
    "\n",
    "train_data['Embarked'].replace('S', 0.0, inplace=True)\n",
    "test_data['Embarked'].replace('S', 0.0, inplace=True)\n",
    "\n",
    "train_data['Embarked'].replace('C', 1.0, inplace=True)\n",
    "test_data['Embarked'].replace('C', 1.0, inplace=True)\n",
    "\n",
    "train_data['Embarked'].replace('Q', 2.0, inplace=True)\n",
    "test_data['Embarked'].replace('Q', 2.0, inplace=True)\n",
    "\n",
    "train_data['Embarked'].fillna(test_data['Embarked'].mean(), inplace=True)  # 用平均值填充空白"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Survived = train_data.pop('Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们使用batch-normalization对数据进行标准化处理，使其服从正态分布："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "train_data = train_data.values[:, :]  # 将数据转化为numpy-array\n",
    "test_data = test_data.values[:, :]\n",
    "# batch-normalization算法\n",
    "def simple_batch_norm_1d(x, gamma, beta):  \n",
    "    eps = 1e-5\n",
    "    x_mean = np.mean(x, axis=0) # 平均值\n",
    "    x_var = np.mean((x - x_mean) ** 2, axis=0)  # 方差\n",
    "    x_hat = (x - x_mean) / np.sqrt(x_var + eps)  \n",
    "    return gamma * x_hat + beta  # 标准正态化\n",
    "# 测试集\n",
    "x_train = train_data\n",
    "gamma = np.ones(x_train.shape[1])\n",
    "beta = np.zeros(x_train.shape[1])\n",
    "y_train = simple_batch_norm_1d(x_train, gamma, beta)\n",
    "# 训练集\n",
    "x_test = test_data\n",
    "gamma = np.ones(x_test.shape[1])\n",
    "beta = np.zeros(x_test.shape[1])\n",
    "y_test = simple_batch_norm_1d(x_test, gamma, beta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.模型选择\n",
    "\n",
    "    先使用逻辑回归进行预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8013468013468014\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model.logistic import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "submit1 = pd.read_csv(\"Logistic_submission.csv\")\n",
    "# Survived转化为numpy-array\n",
    "Survived = pd.DataFrame(Survived.iloc[:]).to_numpy()\n",
    "lr = LogisticRegression()\n",
    "lr.fit(y_train, Survived)  # 训练\n",
    "pre_train = lr.predict(y_train) # 使用训练数据做预测\n",
    "acc = accuracy_score(Survived, pre_train)  # 评估精度\n",
    "print(acc)\n",
    "pre_test = lr.predict(y_test)  # 使用测试数据预测\n",
    "submit1['Survived'] = pre_test\n",
    "submit1.to_csv('Logistic_submission.csv', index=False)  # 结果保存在Logistic_submission.csv中"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后我们使用全连接神经网络进行尝试："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0  Loss = 0.652106\n",
      "epoch 1  Loss = 0.642692\n",
      "epoch 2  Loss = 0.633125\n",
      "epoch 3  Loss = 0.623050\n",
      "epoch 4  Loss = 0.612309\n",
      "epoch 5  Loss = 0.600859\n",
      "epoch 6  Loss = 0.588774\n",
      "epoch 7  Loss = 0.576225\n",
      "epoch 8  Loss = 0.563462\n",
      "epoch 9  Loss = 0.550778\n",
      "epoch 10  Loss = 0.538475\n",
      "epoch 11  Loss = 0.526825\n",
      "epoch 12  Loss = 0.516040\n",
      "epoch 13  Loss = 0.506260\n",
      "epoch 14  Loss = 0.497549\n",
      "epoch 15  Loss = 0.489906\n",
      "epoch 16  Loss = 0.483283\n",
      "epoch 17  Loss = 0.477595\n",
      "epoch 18  Loss = 0.472743\n",
      "epoch 19  Loss = 0.468622\n",
      "epoch 20  Loss = 0.465130\n",
      "epoch 21  Loss = 0.462172\n",
      "epoch 22  Loss = 0.459663\n",
      "epoch 23  Loss = 0.457532\n",
      "epoch 24  Loss = 0.455716\n",
      "epoch 25  Loss = 0.454162\n",
      "epoch 26  Loss = 0.452829\n",
      "epoch 27  Loss = 0.451680\n",
      "epoch 28  Loss = 0.450685\n",
      "epoch 29  Loss = 0.449820\n",
      "epoch 30  Loss = 0.449065\n",
      "epoch 31  Loss = 0.448403\n",
      "epoch 32  Loss = 0.447820\n",
      "epoch 33  Loss = 0.447305\n",
      "epoch 34  Loss = 0.446849\n",
      "epoch 35  Loss = 0.446442\n",
      "epoch 36  Loss = 0.446079\n",
      "epoch 37  Loss = 0.445754\n",
      "epoch 38  Loss = 0.445461\n",
      "epoch 39  Loss = 0.445196\n",
      "epoch 40  Loss = 0.444957\n",
      "epoch 41  Loss = 0.444740\n",
      "epoch 42  Loss = 0.444541\n",
      "epoch 43  Loss = 0.444360\n",
      "epoch 44  Loss = 0.444193\n",
      "epoch 45  Loss = 0.444040\n",
      "epoch 46  Loss = 0.443899\n",
      "epoch 47  Loss = 0.443768\n",
      "epoch 48  Loss = 0.443646\n",
      "epoch 49  Loss = 0.443533\n",
      "epoch 50  Loss = 0.443427\n",
      "epoch 51  Loss = 0.443328\n",
      "epoch 52  Loss = 0.443234\n",
      "epoch 53  Loss = 0.443146\n",
      "epoch 54  Loss = 0.443063\n",
      "epoch 55  Loss = 0.442984\n",
      "epoch 56  Loss = 0.442909\n",
      "epoch 57  Loss = 0.442838\n",
      "epoch 58  Loss = 0.442770\n",
      "epoch 59  Loss = 0.442704\n",
      "epoch 60  Loss = 0.442641\n",
      "epoch 61  Loss = 0.442581\n",
      "epoch 62  Loss = 0.442523\n",
      "epoch 63  Loss = 0.442466\n",
      "epoch 64  Loss = 0.442412\n",
      "epoch 65  Loss = 0.442359\n",
      "epoch 66  Loss = 0.442308\n",
      "epoch 67  Loss = 0.442257\n",
      "epoch 68  Loss = 0.442209\n",
      "epoch 69  Loss = 0.442161\n",
      "epoch 70  Loss = 0.442114\n",
      "epoch 71  Loss = 0.442068\n",
      "epoch 72  Loss = 0.442024\n",
      "epoch 73  Loss = 0.441980\n",
      "epoch 74  Loss = 0.441936\n",
      "epoch 75  Loss = 0.441894\n",
      "epoch 76  Loss = 0.441852\n",
      "epoch 77  Loss = 0.441810\n",
      "epoch 78  Loss = 0.441769\n",
      "epoch 79  Loss = 0.441729\n",
      "epoch 80  Loss = 0.441689\n",
      "epoch 81  Loss = 0.441649\n",
      "epoch 82  Loss = 0.441610\n",
      "epoch 83  Loss = 0.441571\n",
      "epoch 84  Loss = 0.441533\n",
      "epoch 85  Loss = 0.441495\n",
      "epoch 86  Loss = 0.441457\n",
      "epoch 87  Loss = 0.441419\n",
      "epoch 88  Loss = 0.441382\n",
      "epoch 89  Loss = 0.441344\n",
      "epoch 90  Loss = 0.441307\n",
      "epoch 91  Loss = 0.441270\n",
      "epoch 92  Loss = 0.441234\n",
      "epoch 93  Loss = 0.441197\n",
      "epoch 94  Loss = 0.441161\n",
      "epoch 95  Loss = 0.441124\n",
      "epoch 96  Loss = 0.441088\n",
      "epoch 97  Loss = 0.441052\n",
      "epoch 98  Loss = 0.441016\n",
      "epoch 99  Loss = 0.440980\n",
      "epoch 100  Loss = 0.440944\n",
      "epoch 101  Loss = 0.440908\n",
      "epoch 102  Loss = 0.440872\n",
      "epoch 103  Loss = 0.440836\n",
      "epoch 104  Loss = 0.440801\n",
      "epoch 105  Loss = 0.440765\n",
      "epoch 106  Loss = 0.440729\n",
      "epoch 107  Loss = 0.440693\n",
      "epoch 108  Loss = 0.440658\n",
      "epoch 109  Loss = 0.440622\n",
      "epoch 110  Loss = 0.440586\n",
      "epoch 111  Loss = 0.440551\n",
      "epoch 112  Loss = 0.440515\n",
      "epoch 113  Loss = 0.440479\n",
      "epoch 114  Loss = 0.440443\n",
      "epoch 115  Loss = 0.440407\n",
      "epoch 116  Loss = 0.440372\n",
      "epoch 117  Loss = 0.440336\n",
      "epoch 118  Loss = 0.440300\n",
      "epoch 119  Loss = 0.440264\n",
      "epoch 120  Loss = 0.440228\n",
      "epoch 121  Loss = 0.440192\n",
      "epoch 122  Loss = 0.440155\n",
      "epoch 123  Loss = 0.440119\n",
      "epoch 124  Loss = 0.440083\n",
      "epoch 125  Loss = 0.440047\n",
      "epoch 126  Loss = 0.440010\n",
      "epoch 127  Loss = 0.439974\n",
      "epoch 128  Loss = 0.439937\n",
      "epoch 129  Loss = 0.439901\n",
      "epoch 130  Loss = 0.439864\n",
      "epoch 131  Loss = 0.439827\n",
      "epoch 132  Loss = 0.439790\n",
      "epoch 133  Loss = 0.439753\n",
      "epoch 134  Loss = 0.439716\n",
      "epoch 135  Loss = 0.439679\n",
      "epoch 136  Loss = 0.439642\n",
      "epoch 137  Loss = 0.439605\n",
      "epoch 138  Loss = 0.439567\n",
      "epoch 139  Loss = 0.439530\n",
      "epoch 140  Loss = 0.439492\n",
      "epoch 141  Loss = 0.439454\n",
      "epoch 142  Loss = 0.439416\n",
      "epoch 143  Loss = 0.439379\n",
      "epoch 144  Loss = 0.439341\n",
      "epoch 145  Loss = 0.439302\n",
      "epoch 146  Loss = 0.439264\n",
      "epoch 147  Loss = 0.439226\n",
      "epoch 148  Loss = 0.439187\n",
      "epoch 149  Loss = 0.439149\n",
      "epoch 150  Loss = 0.439110\n",
      "epoch 151  Loss = 0.439071\n",
      "epoch 152  Loss = 0.439032\n",
      "epoch 153  Loss = 0.438993\n",
      "epoch 154  Loss = 0.438954\n",
      "epoch 155  Loss = 0.438914\n",
      "epoch 156  Loss = 0.438875\n",
      "epoch 157  Loss = 0.438835\n",
      "epoch 158  Loss = 0.438795\n",
      "epoch 159  Loss = 0.438755\n",
      "epoch 160  Loss = 0.438715\n",
      "epoch 161  Loss = 0.438675\n",
      "epoch 162  Loss = 0.438635\n",
      "epoch 163  Loss = 0.438594\n",
      "epoch 164  Loss = 0.438554\n",
      "epoch 165  Loss = 0.438513\n",
      "epoch 166  Loss = 0.438472\n",
      "epoch 167  Loss = 0.438431\n",
      "epoch 168  Loss = 0.438390\n",
      "epoch 169  Loss = 0.438349\n",
      "epoch 170  Loss = 0.438307\n",
      "epoch 171  Loss = 0.438266\n",
      "epoch 172  Loss = 0.438224\n",
      "epoch 173  Loss = 0.438182\n",
      "epoch 174  Loss = 0.438140\n",
      "epoch 175  Loss = 0.438098\n",
      "epoch 176  Loss = 0.438055\n",
      "epoch 177  Loss = 0.438013\n",
      "epoch 178  Loss = 0.437970\n",
      "epoch 179  Loss = 0.437927\n",
      "epoch 180  Loss = 0.437884\n",
      "epoch 181  Loss = 0.437841\n",
      "epoch 182  Loss = 0.437797\n",
      "epoch 183  Loss = 0.437754\n",
      "epoch 184  Loss = 0.437710\n",
      "epoch 185  Loss = 0.437666\n",
      "epoch 186  Loss = 0.437622\n",
      "epoch 187  Loss = 0.437578\n",
      "epoch 188  Loss = 0.437533\n",
      "epoch 189  Loss = 0.437489\n",
      "epoch 190  Loss = 0.437444\n",
      "epoch 191  Loss = 0.437399\n",
      "epoch 192  Loss = 0.437354\n",
      "epoch 193  Loss = 0.437308\n",
      "epoch 194  Loss = 0.437263\n",
      "epoch 195  Loss = 0.437217\n",
      "epoch 196  Loss = 0.437171\n",
      "epoch 197  Loss = 0.437125\n",
      "epoch 198  Loss = 0.437079\n",
      "epoch 199  Loss = 0.437033\n",
      "epoch 200  Loss = 0.436986\n",
      "epoch 201  Loss = 0.436939\n",
      "epoch 202  Loss = 0.436892\n",
      "epoch 203  Loss = 0.436845\n",
      "epoch 204  Loss = 0.436797\n",
      "epoch 205  Loss = 0.436750\n",
      "epoch 206  Loss = 0.436702\n",
      "epoch 207  Loss = 0.436654\n",
      "epoch 208  Loss = 0.436606\n",
      "epoch 209  Loss = 0.436557\n",
      "epoch 210  Loss = 0.436508\n",
      "epoch 211  Loss = 0.436460\n",
      "epoch 212  Loss = 0.436411\n",
      "epoch 213  Loss = 0.436361\n",
      "epoch 214  Loss = 0.436312\n",
      "epoch 215  Loss = 0.436262\n",
      "epoch 216  Loss = 0.436212\n",
      "epoch 217  Loss = 0.436162\n",
      "epoch 218  Loss = 0.436112\n",
      "epoch 219  Loss = 0.436061\n",
      "epoch 220  Loss = 0.436011\n",
      "epoch 221  Loss = 0.435960\n",
      "epoch 222  Loss = 0.435909\n",
      "epoch 223  Loss = 0.435857\n",
      "epoch 224  Loss = 0.435805\n",
      "epoch 225  Loss = 0.435754\n",
      "epoch 226  Loss = 0.435702\n",
      "epoch 227  Loss = 0.435649\n",
      "epoch 228  Loss = 0.435597\n",
      "epoch 229  Loss = 0.435544\n",
      "epoch 230  Loss = 0.435491\n",
      "epoch 231  Loss = 0.435438\n",
      "epoch 232  Loss = 0.435385\n",
      "epoch 233  Loss = 0.435331\n",
      "epoch 234  Loss = 0.435277\n",
      "epoch 235  Loss = 0.435223\n",
      "epoch 236  Loss = 0.435169\n",
      "epoch 237  Loss = 0.435114\n",
      "epoch 238  Loss = 0.435059\n",
      "epoch 239  Loss = 0.435004\n",
      "epoch 240  Loss = 0.434949\n",
      "epoch 241  Loss = 0.434894\n",
      "epoch 242  Loss = 0.434838\n",
      "epoch 243  Loss = 0.434782\n",
      "epoch 244  Loss = 0.434726\n",
      "epoch 245  Loss = 0.434669\n",
      "epoch 246  Loss = 0.434613\n",
      "epoch 247  Loss = 0.434556\n",
      "epoch 248  Loss = 0.434499\n",
      "epoch 249  Loss = 0.434441\n",
      "epoch 250  Loss = 0.434384\n",
      "epoch 251  Loss = 0.434326\n",
      "epoch 252  Loss = 0.434268\n",
      "epoch 253  Loss = 0.434209\n",
      "epoch 254  Loss = 0.434151\n",
      "epoch 255  Loss = 0.434092\n",
      "epoch 256  Loss = 0.434033\n",
      "epoch 257  Loss = 0.433973\n",
      "epoch 258  Loss = 0.433914\n",
      "epoch 259  Loss = 0.433854\n",
      "epoch 260  Loss = 0.433794\n",
      "epoch 261  Loss = 0.433734\n",
      "epoch 262  Loss = 0.433673\n",
      "epoch 263  Loss = 0.433612\n",
      "epoch 264  Loss = 0.433551\n",
      "epoch 265  Loss = 0.433490\n",
      "epoch 266  Loss = 0.433428\n",
      "epoch 267  Loss = 0.433366\n",
      "epoch 268  Loss = 0.433304\n",
      "epoch 269  Loss = 0.433242\n",
      "epoch 270  Loss = 0.433179\n",
      "epoch 271  Loss = 0.433116\n",
      "epoch 272  Loss = 0.433053\n",
      "epoch 273  Loss = 0.432990\n",
      "epoch 274  Loss = 0.432926\n",
      "epoch 275  Loss = 0.432862\n",
      "epoch 276  Loss = 0.432798\n",
      "epoch 277  Loss = 0.432734\n",
      "epoch 278  Loss = 0.432669\n",
      "epoch 279  Loss = 0.432604\n",
      "epoch 280  Loss = 0.432539\n",
      "epoch 281  Loss = 0.432474\n",
      "epoch 282  Loss = 0.432408\n",
      "epoch 283  Loss = 0.432342\n",
      "epoch 284  Loss = 0.432276\n",
      "epoch 285  Loss = 0.432209\n",
      "epoch 286  Loss = 0.432143\n",
      "epoch 287  Loss = 0.432076\n",
      "epoch 288  Loss = 0.432008\n",
      "epoch 289  Loss = 0.431941\n",
      "epoch 290  Loss = 0.431873\n",
      "epoch 291  Loss = 0.431805\n",
      "epoch 292  Loss = 0.431737\n",
      "epoch 293  Loss = 0.431668\n",
      "epoch 294  Loss = 0.431600\n",
      "epoch 295  Loss = 0.431530\n",
      "epoch 296  Loss = 0.431461\n",
      "epoch 297  Loss = 0.431392\n",
      "epoch 298  Loss = 0.431322\n",
      "epoch 299  Loss = 0.431252\n",
      "epoch 300  Loss = 0.431181\n",
      "epoch 301  Loss = 0.431111\n",
      "epoch 302  Loss = 0.431040\n",
      "epoch 303  Loss = 0.430969\n",
      "epoch 304  Loss = 0.430897\n",
      "epoch 305  Loss = 0.430826\n",
      "epoch 306  Loss = 0.430754\n",
      "epoch 307  Loss = 0.430681\n",
      "epoch 308  Loss = 0.430609\n",
      "epoch 309  Loss = 0.430536\n",
      "epoch 310  Loss = 0.430463\n",
      "epoch 311  Loss = 0.430390\n",
      "epoch 312  Loss = 0.430317\n",
      "epoch 313  Loss = 0.430243\n",
      "epoch 314  Loss = 0.430169\n",
      "epoch 315  Loss = 0.430095\n",
      "epoch 316  Loss = 0.430020\n",
      "epoch 317  Loss = 0.429945\n",
      "epoch 318  Loss = 0.429870\n",
      "epoch 319  Loss = 0.429795\n",
      "epoch 320  Loss = 0.429719\n",
      "epoch 321  Loss = 0.429644\n",
      "epoch 322  Loss = 0.429568\n",
      "epoch 323  Loss = 0.429491\n",
      "epoch 324  Loss = 0.429415\n",
      "epoch 325  Loss = 0.429338\n",
      "epoch 326  Loss = 0.429261\n",
      "epoch 327  Loss = 0.429183\n",
      "epoch 328  Loss = 0.429106\n",
      "epoch 329  Loss = 0.429028\n",
      "epoch 330  Loss = 0.428950\n",
      "epoch 331  Loss = 0.428872\n",
      "epoch 332  Loss = 0.428793\n",
      "epoch 333  Loss = 0.428714\n",
      "epoch 334  Loss = 0.428635\n",
      "epoch 335  Loss = 0.428556\n",
      "epoch 336  Loss = 0.428476\n",
      "epoch 337  Loss = 0.428397\n",
      "epoch 338  Loss = 0.428317\n",
      "epoch 339  Loss = 0.428236\n",
      "epoch 340  Loss = 0.428156\n",
      "epoch 341  Loss = 0.428075\n",
      "epoch 342  Loss = 0.427994\n",
      "epoch 343  Loss = 0.427913\n",
      "epoch 344  Loss = 0.427832\n",
      "epoch 345  Loss = 0.427750\n",
      "epoch 346  Loss = 0.427668\n",
      "epoch 347  Loss = 0.427586\n",
      "epoch 348  Loss = 0.427503\n",
      "epoch 349  Loss = 0.427421\n",
      "epoch 350  Loss = 0.427338\n",
      "epoch 351  Loss = 0.427255\n",
      "epoch 352  Loss = 0.427172\n",
      "epoch 353  Loss = 0.427088\n",
      "epoch 354  Loss = 0.427004\n",
      "epoch 355  Loss = 0.426920\n",
      "epoch 356  Loss = 0.426836\n",
      "epoch 357  Loss = 0.426752\n",
      "epoch 358  Loss = 0.426667\n",
      "epoch 359  Loss = 0.426583\n",
      "epoch 360  Loss = 0.426497\n",
      "epoch 361  Loss = 0.426412\n",
      "epoch 362  Loss = 0.426327\n",
      "epoch 363  Loss = 0.426241\n",
      "epoch 364  Loss = 0.426155\n",
      "epoch 365  Loss = 0.426069\n",
      "epoch 366  Loss = 0.425983\n",
      "epoch 367  Loss = 0.425897\n",
      "epoch 368  Loss = 0.425810\n",
      "epoch 369  Loss = 0.425723\n",
      "epoch 370  Loss = 0.425636\n",
      "epoch 371  Loss = 0.425549\n",
      "epoch 372  Loss = 0.425462\n",
      "epoch 373  Loss = 0.425374\n",
      "epoch 374  Loss = 0.425286\n",
      "epoch 375  Loss = 0.425198\n",
      "epoch 376  Loss = 0.425110\n",
      "epoch 377  Loss = 0.425022\n",
      "epoch 378  Loss = 0.424933\n",
      "epoch 379  Loss = 0.424845\n",
      "epoch 380  Loss = 0.424756\n",
      "epoch 381  Loss = 0.424667\n",
      "epoch 382  Loss = 0.424578\n",
      "epoch 383  Loss = 0.424488\n",
      "epoch 384  Loss = 0.424399\n",
      "epoch 385  Loss = 0.424309\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 386  Loss = 0.424220\n",
      "epoch 387  Loss = 0.424130\n",
      "epoch 388  Loss = 0.424039\n",
      "epoch 389  Loss = 0.423949\n",
      "epoch 390  Loss = 0.423859\n",
      "epoch 391  Loss = 0.423768\n",
      "epoch 392  Loss = 0.423678\n",
      "epoch 393  Loss = 0.423587\n",
      "epoch 394  Loss = 0.423496\n",
      "epoch 395  Loss = 0.423405\n",
      "epoch 396  Loss = 0.423314\n",
      "epoch 397  Loss = 0.423222\n",
      "epoch 398  Loss = 0.423131\n",
      "epoch 399  Loss = 0.423039\n",
      "epoch 400  Loss = 0.422947\n",
      "epoch 401  Loss = 0.422856\n",
      "epoch 402  Loss = 0.422764\n",
      "epoch 403  Loss = 0.422671\n",
      "epoch 404  Loss = 0.422579\n",
      "epoch 405  Loss = 0.422487\n",
      "epoch 406  Loss = 0.422395\n",
      "epoch 407  Loss = 0.422302\n",
      "epoch 408  Loss = 0.422209\n",
      "epoch 409  Loss = 0.422117\n",
      "epoch 410  Loss = 0.422024\n",
      "epoch 411  Loss = 0.421931\n",
      "epoch 412  Loss = 0.421838\n",
      "epoch 413  Loss = 0.421745\n",
      "epoch 414  Loss = 0.421651\n",
      "epoch 415  Loss = 0.421558\n",
      "epoch 416  Loss = 0.421465\n",
      "epoch 417  Loss = 0.421371\n",
      "epoch 418  Loss = 0.421278\n",
      "epoch 419  Loss = 0.421184\n",
      "epoch 420  Loss = 0.421090\n",
      "epoch 421  Loss = 0.420996\n",
      "epoch 422  Loss = 0.420902\n",
      "epoch 423  Loss = 0.420808\n",
      "epoch 424  Loss = 0.420714\n",
      "epoch 425  Loss = 0.420620\n",
      "epoch 426  Loss = 0.420526\n",
      "epoch 427  Loss = 0.420432\n",
      "epoch 428  Loss = 0.420337\n",
      "epoch 429  Loss = 0.420243\n",
      "epoch 430  Loss = 0.420149\n",
      "epoch 431  Loss = 0.420054\n",
      "epoch 432  Loss = 0.419959\n",
      "epoch 433  Loss = 0.419865\n",
      "epoch 434  Loss = 0.419770\n",
      "epoch 435  Loss = 0.419675\n",
      "epoch 436  Loss = 0.419580\n",
      "epoch 437  Loss = 0.419485\n",
      "epoch 438  Loss = 0.419391\n",
      "epoch 439  Loss = 0.419296\n",
      "epoch 440  Loss = 0.419200\n",
      "epoch 441  Loss = 0.419105\n",
      "epoch 442  Loss = 0.419010\n",
      "epoch 443  Loss = 0.418915\n",
      "epoch 444  Loss = 0.418820\n",
      "epoch 445  Loss = 0.418724\n",
      "epoch 446  Loss = 0.418629\n",
      "epoch 447  Loss = 0.418534\n",
      "epoch 448  Loss = 0.418438\n",
      "epoch 449  Loss = 0.418343\n",
      "epoch 450  Loss = 0.418247\n",
      "epoch 451  Loss = 0.418151\n",
      "epoch 452  Loss = 0.418056\n",
      "epoch 453  Loss = 0.417960\n",
      "epoch 454  Loss = 0.417864\n",
      "epoch 455  Loss = 0.417768\n",
      "epoch 456  Loss = 0.417673\n",
      "epoch 457  Loss = 0.417577\n",
      "epoch 458  Loss = 0.417481\n",
      "epoch 459  Loss = 0.417385\n",
      "epoch 460  Loss = 0.417289\n",
      "epoch 461  Loss = 0.417192\n",
      "epoch 462  Loss = 0.417096\n",
      "epoch 463  Loss = 0.417000\n",
      "epoch 464  Loss = 0.416904\n",
      "epoch 465  Loss = 0.416807\n",
      "epoch 466  Loss = 0.416711\n",
      "epoch 467  Loss = 0.416615\n",
      "epoch 468  Loss = 0.416518\n",
      "epoch 469  Loss = 0.416422\n",
      "epoch 470  Loss = 0.416325\n",
      "epoch 471  Loss = 0.416228\n",
      "epoch 472  Loss = 0.416132\n",
      "epoch 473  Loss = 0.416035\n",
      "epoch 474  Loss = 0.415938\n",
      "epoch 475  Loss = 0.415841\n",
      "epoch 476  Loss = 0.415744\n",
      "epoch 477  Loss = 0.415647\n",
      "epoch 478  Loss = 0.415550\n",
      "epoch 479  Loss = 0.415453\n",
      "epoch 480  Loss = 0.415356\n",
      "epoch 481  Loss = 0.415259\n",
      "epoch 482  Loss = 0.415161\n",
      "epoch 483  Loss = 0.415064\n",
      "epoch 484  Loss = 0.414967\n",
      "epoch 485  Loss = 0.414869\n",
      "epoch 486  Loss = 0.414771\n",
      "epoch 487  Loss = 0.414674\n",
      "epoch 488  Loss = 0.414576\n",
      "epoch 489  Loss = 0.414479\n",
      "epoch 490  Loss = 0.414381\n",
      "epoch 491  Loss = 0.414283\n",
      "epoch 492  Loss = 0.414185\n",
      "epoch 493  Loss = 0.414087\n",
      "epoch 494  Loss = 0.413989\n",
      "epoch 495  Loss = 0.413891\n",
      "epoch 496  Loss = 0.413793\n",
      "epoch 497  Loss = 0.413695\n",
      "epoch 498  Loss = 0.413597\n",
      "epoch 499  Loss = 0.413499\n",
      "epoch 500  Loss = 0.413401\n",
      "epoch 501  Loss = 0.413303\n",
      "epoch 502  Loss = 0.413205\n",
      "epoch 503  Loss = 0.413106\n",
      "epoch 504  Loss = 0.413008\n",
      "epoch 505  Loss = 0.412910\n",
      "epoch 506  Loss = 0.412812\n",
      "epoch 507  Loss = 0.412713\n",
      "epoch 508  Loss = 0.412615\n",
      "epoch 509  Loss = 0.412517\n",
      "epoch 510  Loss = 0.412419\n",
      "epoch 511  Loss = 0.412321\n",
      "epoch 512  Loss = 0.412223\n",
      "epoch 513  Loss = 0.412125\n",
      "epoch 514  Loss = 0.412027\n",
      "epoch 515  Loss = 0.411929\n",
      "epoch 516  Loss = 0.411831\n",
      "epoch 517  Loss = 0.411733\n",
      "epoch 518  Loss = 0.411635\n",
      "epoch 519  Loss = 0.411538\n",
      "epoch 520  Loss = 0.411440\n",
      "epoch 521  Loss = 0.411343\n",
      "epoch 522  Loss = 0.411245\n",
      "epoch 523  Loss = 0.411148\n",
      "epoch 524  Loss = 0.411051\n",
      "epoch 525  Loss = 0.410953\n",
      "epoch 526  Loss = 0.410856\n",
      "epoch 527  Loss = 0.410759\n",
      "epoch 528  Loss = 0.410663\n",
      "epoch 529  Loss = 0.410566\n",
      "epoch 530  Loss = 0.410469\n",
      "epoch 531  Loss = 0.410373\n",
      "epoch 532  Loss = 0.410276\n",
      "epoch 533  Loss = 0.410180\n",
      "epoch 534  Loss = 0.410084\n",
      "epoch 535  Loss = 0.409988\n",
      "epoch 536  Loss = 0.409892\n",
      "epoch 537  Loss = 0.409796\n",
      "epoch 538  Loss = 0.409701\n",
      "epoch 539  Loss = 0.409605\n",
      "epoch 540  Loss = 0.409510\n",
      "epoch 541  Loss = 0.409414\n",
      "epoch 542  Loss = 0.409319\n",
      "epoch 543  Loss = 0.409224\n",
      "epoch 544  Loss = 0.409130\n",
      "epoch 545  Loss = 0.409035\n",
      "epoch 546  Loss = 0.408940\n",
      "epoch 547  Loss = 0.408846\n",
      "epoch 548  Loss = 0.408752\n",
      "epoch 549  Loss = 0.408658\n",
      "epoch 550  Loss = 0.408564\n",
      "epoch 551  Loss = 0.408470\n",
      "epoch 552  Loss = 0.408376\n",
      "epoch 553  Loss = 0.408282\n",
      "epoch 554  Loss = 0.408189\n",
      "epoch 555  Loss = 0.408096\n",
      "epoch 556  Loss = 0.408002\n",
      "epoch 557  Loss = 0.407909\n",
      "epoch 558  Loss = 0.407816\n",
      "epoch 559  Loss = 0.407724\n",
      "epoch 560  Loss = 0.407631\n",
      "epoch 561  Loss = 0.407539\n",
      "epoch 562  Loss = 0.407446\n",
      "epoch 563  Loss = 0.407354\n",
      "epoch 564  Loss = 0.407262\n",
      "epoch 565  Loss = 0.407170\n",
      "epoch 566  Loss = 0.407078\n",
      "epoch 567  Loss = 0.406986\n",
      "epoch 568  Loss = 0.406895\n",
      "epoch 569  Loss = 0.406803\n",
      "epoch 570  Loss = 0.406712\n",
      "epoch 571  Loss = 0.406620\n",
      "epoch 572  Loss = 0.406529\n",
      "epoch 573  Loss = 0.406438\n",
      "epoch 574  Loss = 0.406347\n",
      "epoch 575  Loss = 0.406257\n",
      "epoch 576  Loss = 0.406166\n",
      "epoch 577  Loss = 0.406075\n",
      "epoch 578  Loss = 0.405985\n",
      "epoch 579  Loss = 0.405895\n",
      "epoch 580  Loss = 0.405805\n",
      "epoch 581  Loss = 0.405714\n",
      "epoch 582  Loss = 0.405625\n",
      "epoch 583  Loss = 0.405535\n",
      "epoch 584  Loss = 0.405445\n",
      "epoch 585  Loss = 0.405355\n",
      "epoch 586  Loss = 0.405266\n",
      "epoch 587  Loss = 0.405177\n",
      "epoch 588  Loss = 0.405087\n",
      "epoch 589  Loss = 0.404998\n",
      "epoch 590  Loss = 0.404909\n",
      "epoch 591  Loss = 0.404820\n",
      "epoch 592  Loss = 0.404732\n",
      "epoch 593  Loss = 0.404643\n",
      "epoch 594  Loss = 0.404554\n",
      "epoch 595  Loss = 0.404466\n",
      "epoch 596  Loss = 0.404378\n",
      "epoch 597  Loss = 0.404290\n",
      "epoch 598  Loss = 0.404201\n",
      "epoch 599  Loss = 0.404114\n",
      "epoch 600  Loss = 0.404026\n",
      "epoch 601  Loss = 0.403938\n",
      "epoch 602  Loss = 0.403851\n",
      "epoch 603  Loss = 0.403763\n",
      "epoch 604  Loss = 0.403676\n",
      "epoch 605  Loss = 0.403589\n",
      "epoch 606  Loss = 0.403502\n",
      "epoch 607  Loss = 0.403415\n",
      "epoch 608  Loss = 0.403328\n",
      "epoch 609  Loss = 0.403241\n",
      "epoch 610  Loss = 0.403155\n",
      "epoch 611  Loss = 0.403069\n",
      "epoch 612  Loss = 0.402982\n",
      "epoch 613  Loss = 0.402896\n",
      "epoch 614  Loss = 0.402810\n",
      "epoch 615  Loss = 0.402725\n",
      "epoch 616  Loss = 0.402639\n",
      "epoch 617  Loss = 0.402553\n",
      "epoch 618  Loss = 0.402468\n",
      "epoch 619  Loss = 0.402383\n",
      "epoch 620  Loss = 0.402298\n",
      "epoch 621  Loss = 0.402213\n",
      "epoch 622  Loss = 0.402128\n",
      "epoch 623  Loss = 0.402044\n",
      "epoch 624  Loss = 0.401959\n",
      "epoch 625  Loss = 0.401875\n",
      "epoch 626  Loss = 0.401791\n",
      "epoch 627  Loss = 0.401707\n",
      "epoch 628  Loss = 0.401623\n",
      "epoch 629  Loss = 0.401540\n",
      "epoch 630  Loss = 0.401456\n",
      "epoch 631  Loss = 0.401373\n",
      "epoch 632  Loss = 0.401290\n",
      "epoch 633  Loss = 0.401207\n",
      "epoch 634  Loss = 0.401124\n",
      "epoch 635  Loss = 0.401041\n",
      "epoch 636  Loss = 0.400959\n",
      "epoch 637  Loss = 0.400876\n",
      "epoch 638  Loss = 0.400794\n",
      "epoch 639  Loss = 0.400712\n",
      "epoch 640  Loss = 0.400630\n",
      "epoch 641  Loss = 0.400549\n",
      "epoch 642  Loss = 0.400467\n",
      "epoch 643  Loss = 0.400386\n",
      "epoch 644  Loss = 0.400304\n",
      "epoch 645  Loss = 0.400223\n",
      "epoch 646  Loss = 0.400143\n",
      "epoch 647  Loss = 0.400062\n",
      "epoch 648  Loss = 0.399981\n",
      "epoch 649  Loss = 0.399901\n",
      "epoch 650  Loss = 0.399821\n",
      "epoch 651  Loss = 0.399741\n",
      "epoch 652  Loss = 0.399661\n",
      "epoch 653  Loss = 0.399581\n",
      "epoch 654  Loss = 0.399501\n",
      "epoch 655  Loss = 0.399422\n",
      "epoch 656  Loss = 0.399343\n",
      "epoch 657  Loss = 0.399264\n",
      "epoch 658  Loss = 0.399185\n",
      "epoch 659  Loss = 0.399106\n",
      "epoch 660  Loss = 0.399028\n",
      "epoch 661  Loss = 0.398949\n",
      "epoch 662  Loss = 0.398871\n",
      "epoch 663  Loss = 0.398793\n",
      "epoch 664  Loss = 0.398715\n",
      "epoch 665  Loss = 0.398637\n",
      "epoch 666  Loss = 0.398559\n",
      "epoch 667  Loss = 0.398482\n",
      "epoch 668  Loss = 0.398405\n",
      "epoch 669  Loss = 0.398328\n",
      "epoch 670  Loss = 0.398251\n",
      "epoch 671  Loss = 0.398174\n",
      "epoch 672  Loss = 0.398097\n",
      "epoch 673  Loss = 0.398021\n",
      "epoch 674  Loss = 0.397944\n",
      "epoch 675  Loss = 0.397868\n",
      "epoch 676  Loss = 0.397792\n",
      "epoch 677  Loss = 0.397716\n",
      "epoch 678  Loss = 0.397640\n",
      "epoch 679  Loss = 0.397565\n",
      "epoch 680  Loss = 0.397489\n",
      "epoch 681  Loss = 0.397414\n",
      "epoch 682  Loss = 0.397339\n",
      "epoch 683  Loss = 0.397264\n",
      "epoch 684  Loss = 0.397189\n",
      "epoch 685  Loss = 0.397115\n",
      "epoch 686  Loss = 0.397040\n",
      "epoch 687  Loss = 0.396966\n",
      "epoch 688  Loss = 0.396891\n",
      "epoch 689  Loss = 0.396817\n",
      "epoch 690  Loss = 0.396744\n",
      "epoch 691  Loss = 0.396670\n",
      "epoch 692  Loss = 0.396596\n",
      "epoch 693  Loss = 0.396523\n",
      "epoch 694  Loss = 0.396450\n",
      "epoch 695  Loss = 0.396376\n",
      "epoch 696  Loss = 0.396303\n",
      "epoch 697  Loss = 0.396231\n",
      "epoch 698  Loss = 0.396158\n",
      "epoch 699  Loss = 0.396085\n",
      "epoch 700  Loss = 0.396013\n",
      "epoch 701  Loss = 0.395941\n",
      "epoch 702  Loss = 0.395869\n",
      "epoch 703  Loss = 0.395797\n",
      "epoch 704  Loss = 0.395725\n",
      "epoch 705  Loss = 0.395653\n",
      "epoch 706  Loss = 0.395582\n",
      "epoch 707  Loss = 0.395510\n",
      "epoch 708  Loss = 0.395439\n",
      "epoch 709  Loss = 0.395368\n",
      "epoch 710  Loss = 0.395297\n",
      "epoch 711  Loss = 0.395226\n",
      "epoch 712  Loss = 0.395156\n",
      "epoch 713  Loss = 0.395085\n",
      "epoch 714  Loss = 0.395015\n",
      "epoch 715  Loss = 0.394945\n",
      "epoch 716  Loss = 0.394875\n",
      "epoch 717  Loss = 0.394805\n",
      "epoch 718  Loss = 0.394735\n",
      "epoch 719  Loss = 0.394666\n",
      "epoch 720  Loss = 0.394596\n",
      "epoch 721  Loss = 0.394527\n",
      "epoch 722  Loss = 0.394458\n",
      "epoch 723  Loss = 0.394389\n",
      "epoch 724  Loss = 0.394320\n",
      "epoch 725  Loss = 0.394251\n",
      "epoch 726  Loss = 0.394183\n",
      "epoch 727  Loss = 0.394115\n",
      "epoch 728  Loss = 0.394046\n",
      "epoch 729  Loss = 0.393978\n",
      "epoch 730  Loss = 0.393910\n",
      "epoch 731  Loss = 0.393842\n",
      "epoch 732  Loss = 0.393775\n",
      "epoch 733  Loss = 0.393707\n",
      "epoch 734  Loss = 0.393640\n",
      "epoch 735  Loss = 0.393573\n",
      "epoch 736  Loss = 0.393506\n",
      "epoch 737  Loss = 0.393439\n",
      "epoch 738  Loss = 0.393372\n",
      "epoch 739  Loss = 0.393305\n",
      "epoch 740  Loss = 0.393239\n",
      "epoch 741  Loss = 0.393172\n",
      "epoch 742  Loss = 0.393106\n",
      "epoch 743  Loss = 0.393040\n",
      "epoch 744  Loss = 0.392974\n",
      "epoch 745  Loss = 0.392909\n",
      "epoch 746  Loss = 0.392843\n",
      "epoch 747  Loss = 0.392778\n",
      "epoch 748  Loss = 0.392712\n",
      "epoch 749  Loss = 0.392647\n",
      "epoch 750  Loss = 0.392582\n",
      "epoch 751  Loss = 0.392517\n",
      "epoch 752  Loss = 0.392452\n",
      "epoch 753  Loss = 0.392388\n",
      "epoch 754  Loss = 0.392323\n",
      "epoch 755  Loss = 0.392259\n",
      "epoch 756  Loss = 0.392195\n",
      "epoch 757  Loss = 0.392131\n",
      "epoch 758  Loss = 0.392067\n",
      "epoch 759  Loss = 0.392004\n",
      "epoch 760  Loss = 0.391940\n",
      "epoch 761  Loss = 0.391877\n",
      "epoch 762  Loss = 0.391813\n",
      "epoch 763  Loss = 0.391750\n",
      "epoch 764  Loss = 0.391687\n",
      "epoch 765  Loss = 0.391625\n",
      "epoch 766  Loss = 0.391562\n",
      "epoch 767  Loss = 0.391499\n",
      "epoch 768  Loss = 0.391437\n",
      "epoch 769  Loss = 0.391375\n",
      "epoch 770  Loss = 0.391313\n",
      "epoch 771  Loss = 0.391251\n",
      "epoch 772  Loss = 0.391189\n",
      "epoch 773  Loss = 0.391128\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 774  Loss = 0.391066\n",
      "epoch 775  Loss = 0.391005\n",
      "epoch 776  Loss = 0.390944\n",
      "epoch 777  Loss = 0.390883\n",
      "epoch 778  Loss = 0.390822\n",
      "epoch 779  Loss = 0.390761\n",
      "epoch 780  Loss = 0.390700\n",
      "epoch 781  Loss = 0.390640\n",
      "epoch 782  Loss = 0.390580\n",
      "epoch 783  Loss = 0.390520\n",
      "epoch 784  Loss = 0.390460\n",
      "epoch 785  Loss = 0.390400\n",
      "epoch 786  Loss = 0.390340\n",
      "epoch 787  Loss = 0.390281\n",
      "epoch 788  Loss = 0.390221\n",
      "epoch 789  Loss = 0.390162\n",
      "epoch 790  Loss = 0.390103\n",
      "epoch 791  Loss = 0.390044\n",
      "epoch 792  Loss = 0.389985\n",
      "epoch 793  Loss = 0.389926\n",
      "epoch 794  Loss = 0.389868\n",
      "epoch 795  Loss = 0.389809\n",
      "epoch 796  Loss = 0.389751\n",
      "epoch 797  Loss = 0.389693\n",
      "epoch 798  Loss = 0.389635\n",
      "epoch 799  Loss = 0.389577\n",
      "epoch 800  Loss = 0.389520\n",
      "epoch 801  Loss = 0.389462\n",
      "epoch 802  Loss = 0.389405\n",
      "epoch 803  Loss = 0.389348\n",
      "epoch 804  Loss = 0.389291\n",
      "epoch 805  Loss = 0.389234\n",
      "epoch 806  Loss = 0.389177\n",
      "epoch 807  Loss = 0.389120\n",
      "epoch 808  Loss = 0.389064\n",
      "epoch 809  Loss = 0.389008\n",
      "epoch 810  Loss = 0.388951\n",
      "epoch 811  Loss = 0.388895\n",
      "epoch 812  Loss = 0.388839\n",
      "epoch 813  Loss = 0.388784\n",
      "epoch 814  Loss = 0.388728\n",
      "epoch 815  Loss = 0.388672\n",
      "epoch 816  Loss = 0.388617\n",
      "epoch 817  Loss = 0.388562\n",
      "epoch 818  Loss = 0.388507\n",
      "epoch 819  Loss = 0.388452\n",
      "epoch 820  Loss = 0.388397\n",
      "epoch 821  Loss = 0.388342\n",
      "epoch 822  Loss = 0.388288\n",
      "epoch 823  Loss = 0.388234\n",
      "epoch 824  Loss = 0.388179\n",
      "epoch 825  Loss = 0.388125\n",
      "epoch 826  Loss = 0.388071\n",
      "epoch 827  Loss = 0.388017\n",
      "epoch 828  Loss = 0.387964\n",
      "epoch 829  Loss = 0.387910\n",
      "epoch 830  Loss = 0.387857\n",
      "epoch 831  Loss = 0.387804\n",
      "epoch 832  Loss = 0.387750\n",
      "epoch 833  Loss = 0.387698\n",
      "epoch 834  Loss = 0.387645\n",
      "epoch 835  Loss = 0.387592\n",
      "epoch 836  Loss = 0.387539\n",
      "epoch 837  Loss = 0.387487\n",
      "epoch 838  Loss = 0.387435\n",
      "epoch 839  Loss = 0.387382\n",
      "epoch 840  Loss = 0.387330\n",
      "epoch 841  Loss = 0.387278\n",
      "epoch 842  Loss = 0.387227\n",
      "epoch 843  Loss = 0.387175\n",
      "epoch 844  Loss = 0.387123\n",
      "epoch 845  Loss = 0.387072\n",
      "epoch 846  Loss = 0.387021\n",
      "epoch 847  Loss = 0.386970\n",
      "epoch 848  Loss = 0.386919\n",
      "epoch 849  Loss = 0.386868\n",
      "epoch 850  Loss = 0.386817\n",
      "epoch 851  Loss = 0.386766\n",
      "epoch 852  Loss = 0.386716\n",
      "epoch 853  Loss = 0.386666\n",
      "epoch 854  Loss = 0.386615\n",
      "epoch 855  Loss = 0.386565\n",
      "epoch 856  Loss = 0.386515\n",
      "epoch 857  Loss = 0.386465\n",
      "epoch 858  Loss = 0.386416\n",
      "epoch 859  Loss = 0.386366\n",
      "epoch 860  Loss = 0.386317\n",
      "epoch 861  Loss = 0.386267\n",
      "epoch 862  Loss = 0.386218\n",
      "epoch 863  Loss = 0.386169\n",
      "epoch 864  Loss = 0.386120\n",
      "epoch 865  Loss = 0.386071\n",
      "epoch 866  Loss = 0.386022\n",
      "epoch 867  Loss = 0.385973\n",
      "epoch 868  Loss = 0.385925\n",
      "epoch 869  Loss = 0.385877\n",
      "epoch 870  Loss = 0.385828\n",
      "epoch 871  Loss = 0.385780\n",
      "epoch 872  Loss = 0.385732\n",
      "epoch 873  Loss = 0.385684\n",
      "epoch 874  Loss = 0.385636\n",
      "epoch 875  Loss = 0.385588\n",
      "epoch 876  Loss = 0.385541\n",
      "epoch 877  Loss = 0.385493\n",
      "epoch 878  Loss = 0.385446\n",
      "epoch 879  Loss = 0.385399\n",
      "epoch 880  Loss = 0.385351\n",
      "epoch 881  Loss = 0.385304\n",
      "epoch 882  Loss = 0.385257\n",
      "epoch 883  Loss = 0.385211\n",
      "epoch 884  Loss = 0.385164\n",
      "epoch 885  Loss = 0.385117\n",
      "epoch 886  Loss = 0.385071\n",
      "epoch 887  Loss = 0.385024\n",
      "epoch 888  Loss = 0.384978\n",
      "epoch 889  Loss = 0.384932\n",
      "epoch 890  Loss = 0.384886\n",
      "epoch 891  Loss = 0.384840\n",
      "epoch 892  Loss = 0.384794\n",
      "epoch 893  Loss = 0.384748\n",
      "epoch 894  Loss = 0.384702\n",
      "epoch 895  Loss = 0.384657\n",
      "epoch 896  Loss = 0.384611\n",
      "epoch 897  Loss = 0.384566\n",
      "epoch 898  Loss = 0.384521\n",
      "epoch 899  Loss = 0.384475\n",
      "epoch 900  Loss = 0.384430\n",
      "epoch 901  Loss = 0.384385\n",
      "epoch 902  Loss = 0.384341\n",
      "epoch 903  Loss = 0.384296\n",
      "epoch 904  Loss = 0.384251\n",
      "epoch 905  Loss = 0.384206\n",
      "epoch 906  Loss = 0.384162\n",
      "epoch 907  Loss = 0.384118\n",
      "epoch 908  Loss = 0.384073\n",
      "epoch 909  Loss = 0.384029\n",
      "epoch 910  Loss = 0.383985\n",
      "epoch 911  Loss = 0.383941\n",
      "epoch 912  Loss = 0.383897\n",
      "epoch 913  Loss = 0.383853\n",
      "epoch 914  Loss = 0.383809\n",
      "epoch 915  Loss = 0.383765\n",
      "epoch 916  Loss = 0.383722\n",
      "epoch 917  Loss = 0.383678\n",
      "epoch 918  Loss = 0.383635\n",
      "epoch 919  Loss = 0.383592\n",
      "epoch 920  Loss = 0.383548\n",
      "epoch 921  Loss = 0.383505\n",
      "epoch 922  Loss = 0.383462\n",
      "epoch 923  Loss = 0.383419\n",
      "epoch 924  Loss = 0.383376\n",
      "epoch 925  Loss = 0.383334\n",
      "epoch 926  Loss = 0.383291\n",
      "epoch 927  Loss = 0.383248\n",
      "epoch 928  Loss = 0.383206\n",
      "epoch 929  Loss = 0.383163\n",
      "epoch 930  Loss = 0.383121\n",
      "epoch 931  Loss = 0.383079\n",
      "epoch 932  Loss = 0.383036\n",
      "epoch 933  Loss = 0.382994\n",
      "epoch 934  Loss = 0.382952\n",
      "epoch 935  Loss = 0.382910\n",
      "epoch 936  Loss = 0.382868\n",
      "epoch 937  Loss = 0.382826\n",
      "epoch 938  Loss = 0.382785\n",
      "epoch 939  Loss = 0.382743\n",
      "epoch 940  Loss = 0.382702\n",
      "epoch 941  Loss = 0.382660\n",
      "epoch 942  Loss = 0.382619\n",
      "epoch 943  Loss = 0.382577\n",
      "epoch 944  Loss = 0.382536\n",
      "epoch 945  Loss = 0.382495\n",
      "epoch 946  Loss = 0.382454\n",
      "epoch 947  Loss = 0.382413\n",
      "epoch 948  Loss = 0.382372\n",
      "epoch 949  Loss = 0.382331\n",
      "epoch 950  Loss = 0.382290\n",
      "epoch 951  Loss = 0.382250\n",
      "epoch 952  Loss = 0.382209\n",
      "epoch 953  Loss = 0.382168\n",
      "epoch 954  Loss = 0.382128\n",
      "epoch 955  Loss = 0.382088\n",
      "epoch 956  Loss = 0.382047\n",
      "epoch 957  Loss = 0.382007\n",
      "epoch 958  Loss = 0.381967\n",
      "epoch 959  Loss = 0.381927\n",
      "epoch 960  Loss = 0.381887\n",
      "epoch 961  Loss = 0.381847\n",
      "epoch 962  Loss = 0.381807\n",
      "epoch 963  Loss = 0.381767\n",
      "epoch 964  Loss = 0.381728\n",
      "epoch 965  Loss = 0.381688\n",
      "epoch 966  Loss = 0.381648\n",
      "epoch 967  Loss = 0.381609\n",
      "epoch 968  Loss = 0.381570\n",
      "epoch 969  Loss = 0.381530\n",
      "epoch 970  Loss = 0.381491\n",
      "epoch 971  Loss = 0.381452\n",
      "epoch 972  Loss = 0.381413\n",
      "epoch 973  Loss = 0.381374\n",
      "epoch 974  Loss = 0.381335\n",
      "epoch 975  Loss = 0.381296\n",
      "epoch 976  Loss = 0.381257\n",
      "epoch 977  Loss = 0.381218\n",
      "epoch 978  Loss = 0.381180\n",
      "epoch 979  Loss = 0.381141\n",
      "epoch 980  Loss = 0.381103\n",
      "epoch 981  Loss = 0.381064\n",
      "epoch 982  Loss = 0.381026\n",
      "epoch 983  Loss = 0.380988\n",
      "epoch 984  Loss = 0.380949\n",
      "epoch 985  Loss = 0.380911\n",
      "epoch 986  Loss = 0.380873\n",
      "epoch 987  Loss = 0.380835\n",
      "epoch 988  Loss = 0.380797\n",
      "epoch 989  Loss = 0.380760\n",
      "epoch 990  Loss = 0.380722\n",
      "epoch 991  Loss = 0.380684\n",
      "epoch 992  Loss = 0.380646\n",
      "epoch 993  Loss = 0.380609\n",
      "epoch 994  Loss = 0.380571\n",
      "epoch 995  Loss = 0.380534\n",
      "epoch 996  Loss = 0.380497\n",
      "epoch 997  Loss = 0.380459\n",
      "epoch 998  Loss = 0.380422\n",
      "epoch 999  Loss = 0.380385\n",
      "epoch 1000  Loss = 0.380348\n",
      "epoch 1001  Loss = 0.380311\n",
      "epoch 1002  Loss = 0.380274\n",
      "epoch 1003  Loss = 0.380237\n",
      "epoch 1004  Loss = 0.380201\n",
      "epoch 1005  Loss = 0.380164\n",
      "epoch 1006  Loss = 0.380127\n",
      "epoch 1007  Loss = 0.380091\n",
      "epoch 1008  Loss = 0.380054\n",
      "epoch 1009  Loss = 0.380018\n",
      "epoch 1010  Loss = 0.379981\n",
      "epoch 1011  Loss = 0.379945\n",
      "epoch 1012  Loss = 0.379909\n",
      "epoch 1013  Loss = 0.379873\n",
      "epoch 1014  Loss = 0.379837\n",
      "epoch 1015  Loss = 0.379801\n",
      "epoch 1016  Loss = 0.379765\n",
      "epoch 1017  Loss = 0.379729\n",
      "epoch 1018  Loss = 0.379693\n",
      "epoch 1019  Loss = 0.379658\n",
      "epoch 1020  Loss = 0.379622\n",
      "epoch 1021  Loss = 0.379586\n",
      "epoch 1022  Loss = 0.379551\n",
      "epoch 1023  Loss = 0.379515\n",
      "epoch 1024  Loss = 0.379480\n",
      "epoch 1025  Loss = 0.379445\n",
      "epoch 1026  Loss = 0.379410\n",
      "epoch 1027  Loss = 0.379375\n",
      "epoch 1028  Loss = 0.379339\n",
      "epoch 1029  Loss = 0.379304\n",
      "epoch 1030  Loss = 0.379269\n",
      "epoch 1031  Loss = 0.379235\n",
      "epoch 1032  Loss = 0.379200\n",
      "epoch 1033  Loss = 0.379165\n",
      "epoch 1034  Loss = 0.379130\n",
      "epoch 1035  Loss = 0.379096\n",
      "epoch 1036  Loss = 0.379061\n",
      "epoch 1037  Loss = 0.379027\n",
      "epoch 1038  Loss = 0.378992\n",
      "epoch 1039  Loss = 0.378958\n",
      "epoch 1040  Loss = 0.378924\n",
      "epoch 1041  Loss = 0.378890\n",
      "epoch 1042  Loss = 0.378856\n",
      "epoch 1043  Loss = 0.378822\n",
      "epoch 1044  Loss = 0.378788\n",
      "epoch 1045  Loss = 0.378754\n",
      "epoch 1046  Loss = 0.378720\n",
      "epoch 1047  Loss = 0.378686\n",
      "epoch 1048  Loss = 0.378652\n",
      "epoch 1049  Loss = 0.378619\n",
      "epoch 1050  Loss = 0.378585\n",
      "epoch 1051  Loss = 0.378552\n",
      "epoch 1052  Loss = 0.378518\n",
      "epoch 1053  Loss = 0.378485\n",
      "epoch 1054  Loss = 0.378451\n",
      "epoch 1055  Loss = 0.378418\n",
      "epoch 1056  Loss = 0.378385\n",
      "epoch 1057  Loss = 0.378352\n",
      "epoch 1058  Loss = 0.378319\n",
      "epoch 1059  Loss = 0.378286\n",
      "epoch 1060  Loss = 0.378253\n",
      "epoch 1061  Loss = 0.378220\n",
      "epoch 1062  Loss = 0.378188\n",
      "epoch 1063  Loss = 0.378155\n",
      "epoch 1064  Loss = 0.378122\n",
      "epoch 1065  Loss = 0.378090\n",
      "epoch 1066  Loss = 0.378057\n",
      "epoch 1067  Loss = 0.378025\n",
      "epoch 1068  Loss = 0.377992\n",
      "epoch 1069  Loss = 0.377960\n",
      "epoch 1070  Loss = 0.377928\n",
      "epoch 1071  Loss = 0.377896\n",
      "epoch 1072  Loss = 0.377863\n",
      "epoch 1073  Loss = 0.377831\n",
      "epoch 1074  Loss = 0.377800\n",
      "epoch 1075  Loss = 0.377768\n",
      "epoch 1076  Loss = 0.377736\n",
      "epoch 1077  Loss = 0.377704\n",
      "epoch 1078  Loss = 0.377672\n",
      "epoch 1079  Loss = 0.377641\n",
      "epoch 1080  Loss = 0.377609\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1081  Loss = 0.377578\n",
      "epoch 1082  Loss = 0.377546\n",
      "epoch 1083  Loss = 0.377515\n",
      "epoch 1084  Loss = 0.377483\n",
      "epoch 1085  Loss = 0.377452\n",
      "epoch 1086  Loss = 0.377421\n",
      "epoch 1087  Loss = 0.377390\n",
      "epoch 1088  Loss = 0.377359\n",
      "epoch 1089  Loss = 0.377328\n",
      "epoch 1090  Loss = 0.377297\n",
      "epoch 1091  Loss = 0.377266\n",
      "epoch 1092  Loss = 0.377235\n",
      "epoch 1093  Loss = 0.377205\n",
      "epoch 1094  Loss = 0.377174\n",
      "epoch 1095  Loss = 0.377143\n",
      "epoch 1096  Loss = 0.377113\n",
      "epoch 1097  Loss = 0.377082\n",
      "epoch 1098  Loss = 0.377052\n",
      "epoch 1099  Loss = 0.377022\n",
      "epoch 1100  Loss = 0.376991\n",
      "epoch 1101  Loss = 0.376961\n",
      "epoch 1102  Loss = 0.376931\n",
      "epoch 1103  Loss = 0.376901\n",
      "epoch 1104  Loss = 0.376871\n",
      "epoch 1105  Loss = 0.376841\n",
      "epoch 1106  Loss = 0.376811\n",
      "epoch 1107  Loss = 0.376781\n",
      "epoch 1108  Loss = 0.376751\n",
      "epoch 1109  Loss = 0.376722\n",
      "epoch 1110  Loss = 0.376692\n",
      "epoch 1111  Loss = 0.376662\n",
      "epoch 1112  Loss = 0.376633\n",
      "epoch 1113  Loss = 0.376603\n",
      "epoch 1114  Loss = 0.376574\n",
      "epoch 1115  Loss = 0.376545\n",
      "epoch 1116  Loss = 0.376515\n",
      "epoch 1117  Loss = 0.376486\n",
      "epoch 1118  Loss = 0.376457\n",
      "epoch 1119  Loss = 0.376428\n",
      "epoch 1120  Loss = 0.376399\n",
      "epoch 1121  Loss = 0.376370\n",
      "epoch 1122  Loss = 0.376341\n",
      "epoch 1123  Loss = 0.376312\n",
      "epoch 1124  Loss = 0.376283\n",
      "epoch 1125  Loss = 0.376255\n",
      "epoch 1126  Loss = 0.376226\n",
      "epoch 1127  Loss = 0.376197\n",
      "epoch 1128  Loss = 0.376169\n",
      "epoch 1129  Loss = 0.376140\n",
      "epoch 1130  Loss = 0.376112\n",
      "epoch 1131  Loss = 0.376084\n",
      "epoch 1132  Loss = 0.376055\n",
      "epoch 1133  Loss = 0.376027\n",
      "epoch 1134  Loss = 0.375999\n",
      "epoch 1135  Loss = 0.375971\n",
      "epoch 1136  Loss = 0.375943\n",
      "epoch 1137  Loss = 0.375915\n",
      "epoch 1138  Loss = 0.375887\n",
      "epoch 1139  Loss = 0.375859\n",
      "epoch 1140  Loss = 0.375831\n",
      "epoch 1141  Loss = 0.375803\n",
      "epoch 1142  Loss = 0.375775\n",
      "epoch 1143  Loss = 0.375748\n",
      "epoch 1144  Loss = 0.375720\n",
      "epoch 1145  Loss = 0.375693\n",
      "epoch 1146  Loss = 0.375665\n",
      "epoch 1147  Loss = 0.375638\n",
      "epoch 1148  Loss = 0.375610\n",
      "epoch 1149  Loss = 0.375583\n",
      "epoch 1150  Loss = 0.375556\n",
      "epoch 1151  Loss = 0.375529\n",
      "epoch 1152  Loss = 0.375501\n",
      "epoch 1153  Loss = 0.375474\n",
      "epoch 1154  Loss = 0.375447\n",
      "epoch 1155  Loss = 0.375420\n",
      "epoch 1156  Loss = 0.375393\n",
      "epoch 1157  Loss = 0.375367\n",
      "epoch 1158  Loss = 0.375340\n",
      "epoch 1159  Loss = 0.375313\n",
      "epoch 1160  Loss = 0.375286\n",
      "epoch 1161  Loss = 0.375260\n",
      "epoch 1162  Loss = 0.375233\n",
      "epoch 1163  Loss = 0.375207\n",
      "epoch 1164  Loss = 0.375180\n",
      "epoch 1165  Loss = 0.375154\n",
      "epoch 1166  Loss = 0.375127\n",
      "epoch 1167  Loss = 0.375101\n",
      "epoch 1168  Loss = 0.375075\n",
      "epoch 1169  Loss = 0.375048\n",
      "epoch 1170  Loss = 0.375022\n",
      "epoch 1171  Loss = 0.374996\n",
      "epoch 1172  Loss = 0.374970\n",
      "epoch 1173  Loss = 0.374944\n",
      "epoch 1174  Loss = 0.374918\n",
      "epoch 1175  Loss = 0.374892\n",
      "epoch 1176  Loss = 0.374867\n",
      "epoch 1177  Loss = 0.374841\n",
      "epoch 1178  Loss = 0.374815\n",
      "epoch 1179  Loss = 0.374789\n",
      "epoch 1180  Loss = 0.374764\n",
      "epoch 1181  Loss = 0.374738\n",
      "epoch 1182  Loss = 0.374713\n",
      "epoch 1183  Loss = 0.374687\n",
      "epoch 1184  Loss = 0.374662\n",
      "epoch 1185  Loss = 0.374637\n",
      "epoch 1186  Loss = 0.374611\n",
      "epoch 1187  Loss = 0.374586\n",
      "epoch 1188  Loss = 0.374561\n",
      "epoch 1189  Loss = 0.374536\n",
      "epoch 1190  Loss = 0.374511\n",
      "epoch 1191  Loss = 0.374485\n",
      "epoch 1192  Loss = 0.374460\n",
      "epoch 1193  Loss = 0.374436\n",
      "epoch 1194  Loss = 0.374411\n",
      "epoch 1195  Loss = 0.374386\n",
      "epoch 1196  Loss = 0.374361\n",
      "epoch 1197  Loss = 0.374336\n",
      "epoch 1198  Loss = 0.374312\n",
      "epoch 1199  Loss = 0.374287\n",
      "epoch 1200  Loss = 0.374262\n",
      "epoch 1201  Loss = 0.374238\n",
      "epoch 1202  Loss = 0.374213\n",
      "epoch 1203  Loss = 0.374189\n",
      "epoch 1204  Loss = 0.374164\n",
      "epoch 1205  Loss = 0.374140\n",
      "epoch 1206  Loss = 0.374116\n",
      "epoch 1207  Loss = 0.374092\n",
      "epoch 1208  Loss = 0.374067\n",
      "epoch 1209  Loss = 0.374043\n",
      "epoch 1210  Loss = 0.374019\n",
      "epoch 1211  Loss = 0.373995\n",
      "epoch 1212  Loss = 0.373971\n",
      "epoch 1213  Loss = 0.373947\n",
      "epoch 1214  Loss = 0.373923\n",
      "epoch 1215  Loss = 0.373899\n",
      "epoch 1216  Loss = 0.373875\n",
      "epoch 1217  Loss = 0.373852\n",
      "epoch 1218  Loss = 0.373828\n",
      "epoch 1219  Loss = 0.373804\n",
      "epoch 1220  Loss = 0.373781\n",
      "epoch 1221  Loss = 0.373757\n",
      "epoch 1222  Loss = 0.373734\n",
      "epoch 1223  Loss = 0.373710\n",
      "epoch 1224  Loss = 0.373687\n",
      "epoch 1225  Loss = 0.373663\n",
      "epoch 1226  Loss = 0.373640\n",
      "epoch 1227  Loss = 0.373617\n",
      "epoch 1228  Loss = 0.373593\n",
      "epoch 1229  Loss = 0.373570\n",
      "epoch 1230  Loss = 0.373547\n",
      "epoch 1231  Loss = 0.373524\n",
      "epoch 1232  Loss = 0.373501\n",
      "epoch 1233  Loss = 0.373478\n",
      "epoch 1234  Loss = 0.373455\n",
      "epoch 1235  Loss = 0.373432\n",
      "epoch 1236  Loss = 0.373409\n",
      "epoch 1237  Loss = 0.373386\n",
      "epoch 1238  Loss = 0.373363\n",
      "epoch 1239  Loss = 0.373340\n",
      "epoch 1240  Loss = 0.373318\n",
      "epoch 1241  Loss = 0.373295\n",
      "epoch 1242  Loss = 0.373272\n",
      "epoch 1243  Loss = 0.373250\n",
      "epoch 1244  Loss = 0.373227\n",
      "epoch 1245  Loss = 0.373205\n",
      "epoch 1246  Loss = 0.373182\n",
      "epoch 1247  Loss = 0.373160\n",
      "epoch 1248  Loss = 0.373138\n",
      "epoch 1249  Loss = 0.373115\n",
      "epoch 1250  Loss = 0.373093\n",
      "epoch 1251  Loss = 0.373071\n",
      "epoch 1252  Loss = 0.373049\n",
      "epoch 1253  Loss = 0.373026\n",
      "epoch 1254  Loss = 0.373004\n",
      "epoch 1255  Loss = 0.372982\n",
      "epoch 1256  Loss = 0.372960\n",
      "epoch 1257  Loss = 0.372938\n",
      "epoch 1258  Loss = 0.372916\n",
      "epoch 1259  Loss = 0.372894\n",
      "epoch 1260  Loss = 0.372873\n",
      "epoch 1261  Loss = 0.372851\n",
      "epoch 1262  Loss = 0.372829\n",
      "epoch 1263  Loss = 0.372807\n",
      "epoch 1264  Loss = 0.372785\n",
      "epoch 1265  Loss = 0.372764\n",
      "epoch 1266  Loss = 0.372742\n",
      "epoch 1267  Loss = 0.372721\n",
      "epoch 1268  Loss = 0.372699\n",
      "epoch 1269  Loss = 0.372678\n",
      "epoch 1270  Loss = 0.372656\n",
      "epoch 1271  Loss = 0.372635\n",
      "epoch 1272  Loss = 0.372613\n",
      "epoch 1273  Loss = 0.372592\n",
      "epoch 1274  Loss = 0.372571\n",
      "epoch 1275  Loss = 0.372550\n",
      "epoch 1276  Loss = 0.372528\n",
      "epoch 1277  Loss = 0.372507\n",
      "epoch 1278  Loss = 0.372486\n",
      "epoch 1279  Loss = 0.372465\n",
      "epoch 1280  Loss = 0.372444\n",
      "epoch 1281  Loss = 0.372423\n",
      "epoch 1282  Loss = 0.372402\n",
      "epoch 1283  Loss = 0.372381\n",
      "epoch 1284  Loss = 0.372360\n",
      "epoch 1285  Loss = 0.372339\n",
      "epoch 1286  Loss = 0.372319\n",
      "epoch 1287  Loss = 0.372298\n",
      "epoch 1288  Loss = 0.372277\n",
      "epoch 1289  Loss = 0.372256\n",
      "epoch 1290  Loss = 0.372236\n",
      "epoch 1291  Loss = 0.372215\n",
      "epoch 1292  Loss = 0.372194\n",
      "epoch 1293  Loss = 0.372174\n",
      "epoch 1294  Loss = 0.372153\n",
      "epoch 1295  Loss = 0.372133\n",
      "epoch 1296  Loss = 0.372112\n",
      "epoch 1297  Loss = 0.372092\n",
      "epoch 1298  Loss = 0.372072\n",
      "epoch 1299  Loss = 0.372051\n",
      "epoch 1300  Loss = 0.372031\n",
      "epoch 1301  Loss = 0.372011\n",
      "epoch 1302  Loss = 0.371991\n",
      "epoch 1303  Loss = 0.371971\n",
      "epoch 1304  Loss = 0.371950\n",
      "epoch 1305  Loss = 0.371930\n",
      "epoch 1306  Loss = 0.371910\n",
      "epoch 1307  Loss = 0.371890\n",
      "epoch 1308  Loss = 0.371870\n",
      "epoch 1309  Loss = 0.371850\n",
      "epoch 1310  Loss = 0.371830\n",
      "epoch 1311  Loss = 0.371810\n",
      "epoch 1312  Loss = 0.371791\n",
      "epoch 1313  Loss = 0.371771\n",
      "epoch 1314  Loss = 0.371751\n",
      "epoch 1315  Loss = 0.371731\n",
      "epoch 1316  Loss = 0.371712\n",
      "epoch 1317  Loss = 0.371692\n",
      "epoch 1318  Loss = 0.371672\n",
      "epoch 1319  Loss = 0.371653\n",
      "epoch 1320  Loss = 0.371633\n",
      "epoch 1321  Loss = 0.371614\n",
      "epoch 1322  Loss = 0.371594\n",
      "epoch 1323  Loss = 0.371575\n",
      "epoch 1324  Loss = 0.371555\n",
      "epoch 1325  Loss = 0.371536\n",
      "epoch 1326  Loss = 0.371516\n",
      "epoch 1327  Loss = 0.371497\n",
      "epoch 1328  Loss = 0.371478\n",
      "epoch 1329  Loss = 0.371459\n",
      "epoch 1330  Loss = 0.371439\n",
      "epoch 1331  Loss = 0.371420\n",
      "epoch 1332  Loss = 0.371401\n",
      "epoch 1333  Loss = 0.371382\n",
      "epoch 1334  Loss = 0.371363\n",
      "epoch 1335  Loss = 0.371344\n",
      "epoch 1336  Loss = 0.371325\n",
      "epoch 1337  Loss = 0.371306\n",
      "epoch 1338  Loss = 0.371287\n",
      "epoch 1339  Loss = 0.371268\n",
      "epoch 1340  Loss = 0.371249\n",
      "epoch 1341  Loss = 0.371230\n",
      "epoch 1342  Loss = 0.371211\n",
      "epoch 1343  Loss = 0.371193\n",
      "epoch 1344  Loss = 0.371174\n",
      "epoch 1345  Loss = 0.371155\n",
      "epoch 1346  Loss = 0.371136\n",
      "epoch 1347  Loss = 0.371118\n",
      "epoch 1348  Loss = 0.371099\n",
      "epoch 1349  Loss = 0.371080\n",
      "epoch 1350  Loss = 0.371062\n",
      "epoch 1351  Loss = 0.371043\n",
      "epoch 1352  Loss = 0.371025\n",
      "epoch 1353  Loss = 0.371006\n",
      "epoch 1354  Loss = 0.370988\n",
      "epoch 1355  Loss = 0.370970\n",
      "epoch 1356  Loss = 0.370951\n",
      "epoch 1357  Loss = 0.370933\n",
      "epoch 1358  Loss = 0.370915\n",
      "epoch 1359  Loss = 0.370896\n",
      "epoch 1360  Loss = 0.370878\n",
      "epoch 1361  Loss = 0.370860\n",
      "epoch 1362  Loss = 0.370842\n",
      "epoch 1363  Loss = 0.370823\n",
      "epoch 1364  Loss = 0.370805\n",
      "epoch 1365  Loss = 0.370787\n",
      "epoch 1366  Loss = 0.370769\n",
      "epoch 1367  Loss = 0.370751\n",
      "epoch 1368  Loss = 0.370733\n",
      "epoch 1369  Loss = 0.370715\n",
      "epoch 1370  Loss = 0.370697\n",
      "epoch 1371  Loss = 0.370679\n",
      "epoch 1372  Loss = 0.370661\n",
      "epoch 1373  Loss = 0.370643\n",
      "epoch 1374  Loss = 0.370626\n",
      "epoch 1375  Loss = 0.370608\n",
      "epoch 1376  Loss = 0.370590\n",
      "epoch 1377  Loss = 0.370572\n",
      "epoch 1378  Loss = 0.370555\n",
      "epoch 1379  Loss = 0.370537\n",
      "epoch 1380  Loss = 0.370519\n",
      "epoch 1381  Loss = 0.370502\n",
      "epoch 1382  Loss = 0.370484\n",
      "epoch 1383  Loss = 0.370467\n",
      "epoch 1384  Loss = 0.370449\n",
      "epoch 1385  Loss = 0.370431\n",
      "epoch 1386  Loss = 0.370414\n",
      "epoch 1387  Loss = 0.370396\n",
      "epoch 1388  Loss = 0.370379\n",
      "epoch 1389  Loss = 0.370362\n",
      "epoch 1390  Loss = 0.370344\n",
      "epoch 1391  Loss = 0.370327\n",
      "epoch 1392  Loss = 0.370310\n",
      "epoch 1393  Loss = 0.370292\n",
      "epoch 1394  Loss = 0.370275\n",
      "epoch 1395  Loss = 0.370258\n",
      "epoch 1396  Loss = 0.370241\n",
      "epoch 1397  Loss = 0.370223\n",
      "epoch 1398  Loss = 0.370206\n",
      "epoch 1399  Loss = 0.370189\n",
      "epoch 1400  Loss = 0.370172\n",
      "epoch 1401  Loss = 0.370155\n",
      "epoch 1402  Loss = 0.370138\n",
      "epoch 1403  Loss = 0.370121\n",
      "epoch 1404  Loss = 0.370104\n",
      "epoch 1405  Loss = 0.370087\n",
      "epoch 1406  Loss = 0.370070\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1407  Loss = 0.370053\n",
      "epoch 1408  Loss = 0.370036\n",
      "epoch 1409  Loss = 0.370019\n",
      "epoch 1410  Loss = 0.370002\n",
      "epoch 1411  Loss = 0.369986\n",
      "epoch 1412  Loss = 0.369969\n",
      "epoch 1413  Loss = 0.369952\n",
      "epoch 1414  Loss = 0.369935\n",
      "epoch 1415  Loss = 0.369919\n",
      "epoch 1416  Loss = 0.369902\n",
      "epoch 1417  Loss = 0.369885\n",
      "epoch 1418  Loss = 0.369869\n",
      "epoch 1419  Loss = 0.369852\n",
      "epoch 1420  Loss = 0.369835\n",
      "epoch 1421  Loss = 0.369819\n",
      "epoch 1422  Loss = 0.369802\n",
      "epoch 1423  Loss = 0.369786\n",
      "epoch 1424  Loss = 0.369769\n",
      "epoch 1425  Loss = 0.369753\n",
      "epoch 1426  Loss = 0.369736\n",
      "epoch 1427  Loss = 0.369720\n",
      "epoch 1428  Loss = 0.369704\n",
      "epoch 1429  Loss = 0.369687\n",
      "epoch 1430  Loss = 0.369671\n",
      "epoch 1431  Loss = 0.369654\n",
      "epoch 1432  Loss = 0.369638\n",
      "epoch 1433  Loss = 0.369622\n",
      "epoch 1434  Loss = 0.369606\n",
      "epoch 1435  Loss = 0.369589\n",
      "epoch 1436  Loss = 0.369573\n",
      "epoch 1437  Loss = 0.369557\n",
      "epoch 1438  Loss = 0.369541\n",
      "epoch 1439  Loss = 0.369525\n",
      "epoch 1440  Loss = 0.369509\n",
      "epoch 1441  Loss = 0.369492\n",
      "epoch 1442  Loss = 0.369476\n",
      "epoch 1443  Loss = 0.369460\n",
      "epoch 1444  Loss = 0.369444\n",
      "epoch 1445  Loss = 0.369428\n",
      "epoch 1446  Loss = 0.369412\n",
      "epoch 1447  Loss = 0.369396\n",
      "epoch 1448  Loss = 0.369380\n",
      "epoch 1449  Loss = 0.369364\n",
      "epoch 1450  Loss = 0.369349\n",
      "epoch 1451  Loss = 0.369333\n",
      "epoch 1452  Loss = 0.369317\n",
      "epoch 1453  Loss = 0.369301\n",
      "epoch 1454  Loss = 0.369285\n",
      "epoch 1455  Loss = 0.369269\n",
      "epoch 1456  Loss = 0.369254\n",
      "epoch 1457  Loss = 0.369238\n",
      "epoch 1458  Loss = 0.369222\n",
      "epoch 1459  Loss = 0.369206\n",
      "epoch 1460  Loss = 0.369191\n",
      "epoch 1461  Loss = 0.369175\n",
      "epoch 1462  Loss = 0.369159\n",
      "epoch 1463  Loss = 0.369144\n",
      "epoch 1464  Loss = 0.369128\n",
      "epoch 1465  Loss = 0.369113\n",
      "epoch 1466  Loss = 0.369097\n",
      "epoch 1467  Loss = 0.369081\n",
      "epoch 1468  Loss = 0.369066\n",
      "epoch 1469  Loss = 0.369050\n",
      "epoch 1470  Loss = 0.369035\n",
      "epoch 1471  Loss = 0.369020\n",
      "epoch 1472  Loss = 0.369004\n",
      "epoch 1473  Loss = 0.368989\n",
      "epoch 1474  Loss = 0.368973\n",
      "epoch 1475  Loss = 0.368958\n",
      "epoch 1476  Loss = 0.368942\n",
      "epoch 1477  Loss = 0.368927\n",
      "epoch 1478  Loss = 0.368912\n",
      "epoch 1479  Loss = 0.368897\n",
      "epoch 1480  Loss = 0.368881\n",
      "epoch 1481  Loss = 0.368866\n",
      "epoch 1482  Loss = 0.368851\n",
      "epoch 1483  Loss = 0.368835\n",
      "epoch 1484  Loss = 0.368820\n",
      "epoch 1485  Loss = 0.368805\n",
      "epoch 1486  Loss = 0.368790\n",
      "epoch 1487  Loss = 0.368775\n",
      "epoch 1488  Loss = 0.368760\n",
      "epoch 1489  Loss = 0.368744\n",
      "epoch 1490  Loss = 0.368729\n",
      "epoch 1491  Loss = 0.368714\n",
      "epoch 1492  Loss = 0.368699\n",
      "epoch 1493  Loss = 0.368684\n",
      "epoch 1494  Loss = 0.368669\n",
      "epoch 1495  Loss = 0.368654\n",
      "epoch 1496  Loss = 0.368639\n",
      "epoch 1497  Loss = 0.368624\n",
      "epoch 1498  Loss = 0.368609\n",
      "epoch 1499  Loss = 0.368594\n",
      "epoch 1500  Loss = 0.368579\n",
      "epoch 1501  Loss = 0.368564\n",
      "epoch 1502  Loss = 0.368549\n",
      "epoch 1503  Loss = 0.368534\n",
      "epoch 1504  Loss = 0.368520\n",
      "epoch 1505  Loss = 0.368505\n",
      "epoch 1506  Loss = 0.368490\n",
      "epoch 1507  Loss = 0.368475\n",
      "epoch 1508  Loss = 0.368460\n",
      "epoch 1509  Loss = 0.368445\n",
      "epoch 1510  Loss = 0.368431\n",
      "epoch 1511  Loss = 0.368416\n",
      "epoch 1512  Loss = 0.368401\n",
      "epoch 1513  Loss = 0.368387\n",
      "epoch 1514  Loss = 0.368372\n",
      "epoch 1515  Loss = 0.368357\n",
      "epoch 1516  Loss = 0.368342\n",
      "epoch 1517  Loss = 0.368328\n",
      "epoch 1518  Loss = 0.368313\n",
      "epoch 1519  Loss = 0.368299\n",
      "epoch 1520  Loss = 0.368284\n",
      "epoch 1521  Loss = 0.368269\n",
      "epoch 1522  Loss = 0.368255\n",
      "epoch 1523  Loss = 0.368240\n",
      "epoch 1524  Loss = 0.368226\n",
      "epoch 1525  Loss = 0.368211\n",
      "epoch 1526  Loss = 0.368196\n",
      "epoch 1527  Loss = 0.368182\n",
      "epoch 1528  Loss = 0.368168\n",
      "epoch 1529  Loss = 0.368153\n",
      "epoch 1530  Loss = 0.368139\n",
      "epoch 1531  Loss = 0.368124\n",
      "epoch 1532  Loss = 0.368110\n",
      "epoch 1533  Loss = 0.368095\n",
      "epoch 1534  Loss = 0.368081\n",
      "epoch 1535  Loss = 0.368066\n",
      "epoch 1536  Loss = 0.368052\n",
      "epoch 1537  Loss = 0.368038\n",
      "epoch 1538  Loss = 0.368023\n",
      "epoch 1539  Loss = 0.368009\n",
      "epoch 1540  Loss = 0.367995\n",
      "epoch 1541  Loss = 0.367980\n",
      "epoch 1542  Loss = 0.367966\n",
      "epoch 1543  Loss = 0.367952\n",
      "epoch 1544  Loss = 0.367937\n",
      "epoch 1545  Loss = 0.367923\n",
      "epoch 1546  Loss = 0.367909\n",
      "epoch 1547  Loss = 0.367895\n",
      "epoch 1548  Loss = 0.367881\n",
      "epoch 1549  Loss = 0.367866\n",
      "epoch 1550  Loss = 0.367852\n",
      "epoch 1551  Loss = 0.367838\n",
      "epoch 1552  Loss = 0.367824\n",
      "epoch 1553  Loss = 0.367810\n",
      "epoch 1554  Loss = 0.367795\n",
      "epoch 1555  Loss = 0.367781\n",
      "epoch 1556  Loss = 0.367767\n",
      "epoch 1557  Loss = 0.367753\n",
      "epoch 1558  Loss = 0.367739\n",
      "epoch 1559  Loss = 0.367725\n",
      "epoch 1560  Loss = 0.367711\n",
      "epoch 1561  Loss = 0.367697\n",
      "epoch 1562  Loss = 0.367683\n",
      "epoch 1563  Loss = 0.367669\n",
      "epoch 1564  Loss = 0.367655\n",
      "epoch 1565  Loss = 0.367641\n",
      "epoch 1566  Loss = 0.367627\n",
      "epoch 1567  Loss = 0.367613\n",
      "epoch 1568  Loss = 0.367599\n",
      "epoch 1569  Loss = 0.367585\n",
      "epoch 1570  Loss = 0.367571\n",
      "epoch 1571  Loss = 0.367557\n",
      "epoch 1572  Loss = 0.367543\n",
      "epoch 1573  Loss = 0.367529\n",
      "epoch 1574  Loss = 0.367515\n",
      "epoch 1575  Loss = 0.367501\n",
      "epoch 1576  Loss = 0.367487\n",
      "epoch 1577  Loss = 0.367473\n",
      "epoch 1578  Loss = 0.367459\n",
      "epoch 1579  Loss = 0.367446\n",
      "epoch 1580  Loss = 0.367432\n",
      "epoch 1581  Loss = 0.367418\n",
      "epoch 1582  Loss = 0.367404\n",
      "epoch 1583  Loss = 0.367390\n",
      "epoch 1584  Loss = 0.367377\n",
      "epoch 1585  Loss = 0.367363\n",
      "epoch 1586  Loss = 0.367349\n",
      "epoch 1587  Loss = 0.367335\n",
      "epoch 1588  Loss = 0.367321\n",
      "epoch 1589  Loss = 0.367308\n",
      "epoch 1590  Loss = 0.367294\n",
      "epoch 1591  Loss = 0.367280\n",
      "epoch 1592  Loss = 0.367266\n",
      "epoch 1593  Loss = 0.367253\n",
      "epoch 1594  Loss = 0.367239\n",
      "epoch 1595  Loss = 0.367225\n",
      "epoch 1596  Loss = 0.367212\n",
      "epoch 1597  Loss = 0.367198\n",
      "epoch 1598  Loss = 0.367184\n",
      "epoch 1599  Loss = 0.367171\n",
      "epoch 1600  Loss = 0.367157\n",
      "epoch 1601  Loss = 0.367143\n",
      "epoch 1602  Loss = 0.367130\n",
      "epoch 1603  Loss = 0.367116\n",
      "epoch 1604  Loss = 0.367102\n",
      "epoch 1605  Loss = 0.367089\n",
      "epoch 1606  Loss = 0.367075\n",
      "epoch 1607  Loss = 0.367062\n",
      "epoch 1608  Loss = 0.367048\n",
      "epoch 1609  Loss = 0.367034\n",
      "epoch 1610  Loss = 0.367021\n",
      "epoch 1611  Loss = 0.367007\n",
      "epoch 1612  Loss = 0.366994\n",
      "epoch 1613  Loss = 0.366980\n",
      "epoch 1614  Loss = 0.366967\n",
      "epoch 1615  Loss = 0.366953\n",
      "epoch 1616  Loss = 0.366940\n",
      "epoch 1617  Loss = 0.366926\n",
      "epoch 1618  Loss = 0.366913\n",
      "epoch 1619  Loss = 0.366899\n",
      "epoch 1620  Loss = 0.366886\n",
      "epoch 1621  Loss = 0.366872\n",
      "epoch 1622  Loss = 0.366859\n",
      "epoch 1623  Loss = 0.366845\n",
      "epoch 1624  Loss = 0.366832\n",
      "epoch 1625  Loss = 0.366818\n",
      "epoch 1626  Loss = 0.366805\n",
      "epoch 1627  Loss = 0.366791\n",
      "epoch 1628  Loss = 0.366778\n",
      "epoch 1629  Loss = 0.366765\n",
      "epoch 1630  Loss = 0.366751\n",
      "epoch 1631  Loss = 0.366738\n",
      "epoch 1632  Loss = 0.366724\n",
      "epoch 1633  Loss = 0.366711\n",
      "epoch 1634  Loss = 0.366698\n",
      "epoch 1635  Loss = 0.366684\n",
      "epoch 1636  Loss = 0.366671\n",
      "epoch 1637  Loss = 0.366658\n",
      "epoch 1638  Loss = 0.366644\n",
      "epoch 1639  Loss = 0.366631\n",
      "epoch 1640  Loss = 0.366618\n",
      "epoch 1641  Loss = 0.366604\n",
      "epoch 1642  Loss = 0.366591\n",
      "epoch 1643  Loss = 0.366578\n",
      "epoch 1644  Loss = 0.366564\n",
      "epoch 1645  Loss = 0.366551\n",
      "epoch 1646  Loss = 0.366538\n",
      "epoch 1647  Loss = 0.366524\n",
      "epoch 1648  Loss = 0.366511\n",
      "epoch 1649  Loss = 0.366498\n",
      "epoch 1650  Loss = 0.366485\n",
      "epoch 1651  Loss = 0.366471\n",
      "epoch 1652  Loss = 0.366458\n",
      "epoch 1653  Loss = 0.366445\n",
      "epoch 1654  Loss = 0.366432\n",
      "epoch 1655  Loss = 0.366418\n",
      "epoch 1656  Loss = 0.366405\n",
      "epoch 1657  Loss = 0.366392\n",
      "epoch 1658  Loss = 0.366379\n",
      "epoch 1659  Loss = 0.366365\n",
      "epoch 1660  Loss = 0.366352\n",
      "epoch 1661  Loss = 0.366339\n",
      "epoch 1662  Loss = 0.366326\n",
      "epoch 1663  Loss = 0.366313\n",
      "epoch 1664  Loss = 0.366299\n",
      "epoch 1665  Loss = 0.366286\n",
      "epoch 1666  Loss = 0.366273\n",
      "epoch 1667  Loss = 0.366260\n",
      "epoch 1668  Loss = 0.366247\n",
      "epoch 1669  Loss = 0.366233\n",
      "epoch 1670  Loss = 0.366220\n",
      "epoch 1671  Loss = 0.366207\n",
      "epoch 1672  Loss = 0.366194\n",
      "epoch 1673  Loss = 0.366181\n",
      "epoch 1674  Loss = 0.366168\n",
      "epoch 1675  Loss = 0.366155\n",
      "epoch 1676  Loss = 0.366141\n",
      "epoch 1677  Loss = 0.366128\n",
      "epoch 1678  Loss = 0.366115\n",
      "epoch 1679  Loss = 0.366102\n",
      "epoch 1680  Loss = 0.366089\n",
      "epoch 1681  Loss = 0.366076\n",
      "epoch 1682  Loss = 0.366063\n",
      "epoch 1683  Loss = 0.366050\n",
      "epoch 1684  Loss = 0.366037\n",
      "epoch 1685  Loss = 0.366023\n",
      "epoch 1686  Loss = 0.366010\n",
      "epoch 1687  Loss = 0.365997\n",
      "epoch 1688  Loss = 0.365984\n",
      "epoch 1689  Loss = 0.365971\n",
      "epoch 1690  Loss = 0.365958\n",
      "epoch 1691  Loss = 0.365945\n",
      "epoch 1692  Loss = 0.365932\n",
      "epoch 1693  Loss = 0.365919\n",
      "epoch 1694  Loss = 0.365906\n",
      "epoch 1695  Loss = 0.365893\n",
      "epoch 1696  Loss = 0.365880\n",
      "epoch 1697  Loss = 0.365867\n",
      "epoch 1698  Loss = 0.365854\n",
      "epoch 1699  Loss = 0.365841\n",
      "epoch 1700  Loss = 0.365828\n",
      "epoch 1701  Loss = 0.365815\n",
      "epoch 1702  Loss = 0.365802\n",
      "epoch 1703  Loss = 0.365789\n",
      "epoch 1704  Loss = 0.365776\n",
      "epoch 1705  Loss = 0.365763\n",
      "epoch 1706  Loss = 0.365750\n",
      "epoch 1707  Loss = 0.365737\n",
      "epoch 1708  Loss = 0.365724\n",
      "epoch 1709  Loss = 0.365711\n",
      "epoch 1710  Loss = 0.365698\n",
      "epoch 1711  Loss = 0.365685\n",
      "epoch 1712  Loss = 0.365672\n",
      "epoch 1713  Loss = 0.365659\n",
      "epoch 1714  Loss = 0.365646\n",
      "epoch 1715  Loss = 0.365633\n",
      "epoch 1716  Loss = 0.365620\n",
      "epoch 1717  Loss = 0.365607\n",
      "epoch 1718  Loss = 0.365594\n",
      "epoch 1719  Loss = 0.365581\n",
      "epoch 1720  Loss = 0.365568\n",
      "epoch 1721  Loss = 0.365555\n",
      "epoch 1722  Loss = 0.365542\n",
      "epoch 1723  Loss = 0.365529\n",
      "epoch 1724  Loss = 0.365516\n",
      "epoch 1725  Loss = 0.365503\n",
      "epoch 1726  Loss = 0.365490\n",
      "epoch 1727  Loss = 0.365478\n",
      "epoch 1728  Loss = 0.365465\n",
      "epoch 1729  Loss = 0.365452\n",
      "epoch 1730  Loss = 0.365439\n",
      "epoch 1731  Loss = 0.365426\n",
      "epoch 1732  Loss = 0.365413\n",
      "epoch 1733  Loss = 0.365400\n",
      "epoch 1734  Loss = 0.365387\n",
      "epoch 1735  Loss = 0.365374\n",
      "epoch 1736  Loss = 0.365361\n",
      "epoch 1737  Loss = 0.365348\n",
      "epoch 1738  Loss = 0.365335\n",
      "epoch 1739  Loss = 0.365323\n",
      "epoch 1740  Loss = 0.365310\n",
      "epoch 1741  Loss = 0.365297\n",
      "epoch 1742  Loss = 0.365284\n",
      "epoch 1743  Loss = 0.365271\n",
      "epoch 1744  Loss = 0.365258\n",
      "epoch 1745  Loss = 0.365245\n",
      "epoch 1746  Loss = 0.365232\n",
      "epoch 1747  Loss = 0.365220\n",
      "epoch 1748  Loss = 0.365207\n",
      "epoch 1749  Loss = 0.365194\n",
      "epoch 1750  Loss = 0.365181\n",
      "epoch 1751  Loss = 0.365168\n",
      "epoch 1752  Loss = 0.365155\n",
      "epoch 1753  Loss = 0.365142\n",
      "epoch 1754  Loss = 0.365129\n",
      "epoch 1755  Loss = 0.365117\n",
      "epoch 1756  Loss = 0.365104\n",
      "epoch 1757  Loss = 0.365091\n",
      "epoch 1758  Loss = 0.365078\n",
      "epoch 1759  Loss = 0.365065\n",
      "epoch 1760  Loss = 0.365052\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1761  Loss = 0.365040\n",
      "epoch 1762  Loss = 0.365027\n",
      "epoch 1763  Loss = 0.365014\n",
      "epoch 1764  Loss = 0.365001\n",
      "epoch 1765  Loss = 0.364988\n",
      "epoch 1766  Loss = 0.364975\n",
      "epoch 1767  Loss = 0.364963\n",
      "epoch 1768  Loss = 0.364950\n",
      "epoch 1769  Loss = 0.364937\n",
      "epoch 1770  Loss = 0.364924\n",
      "epoch 1771  Loss = 0.364911\n",
      "epoch 1772  Loss = 0.364898\n",
      "epoch 1773  Loss = 0.364886\n",
      "epoch 1774  Loss = 0.364873\n",
      "epoch 1775  Loss = 0.364860\n",
      "epoch 1776  Loss = 0.364847\n",
      "epoch 1777  Loss = 0.364834\n",
      "epoch 1778  Loss = 0.364822\n",
      "epoch 1779  Loss = 0.364809\n",
      "epoch 1780  Loss = 0.364796\n",
      "epoch 1781  Loss = 0.364783\n",
      "epoch 1782  Loss = 0.364770\n",
      "epoch 1783  Loss = 0.364757\n",
      "epoch 1784  Loss = 0.364745\n",
      "epoch 1785  Loss = 0.364732\n",
      "epoch 1786  Loss = 0.364719\n",
      "epoch 1787  Loss = 0.364706\n",
      "epoch 1788  Loss = 0.364694\n",
      "epoch 1789  Loss = 0.364681\n",
      "epoch 1790  Loss = 0.364668\n",
      "epoch 1791  Loss = 0.364655\n",
      "epoch 1792  Loss = 0.364642\n",
      "epoch 1793  Loss = 0.364630\n",
      "epoch 1794  Loss = 0.364617\n",
      "epoch 1795  Loss = 0.364604\n",
      "epoch 1796  Loss = 0.364591\n",
      "epoch 1797  Loss = 0.364578\n",
      "epoch 1798  Loss = 0.364566\n",
      "epoch 1799  Loss = 0.364553\n",
      "epoch 1800  Loss = 0.364540\n",
      "epoch 1801  Loss = 0.364527\n",
      "epoch 1802  Loss = 0.364515\n",
      "epoch 1803  Loss = 0.364502\n",
      "epoch 1804  Loss = 0.364489\n",
      "epoch 1805  Loss = 0.364476\n",
      "epoch 1806  Loss = 0.364463\n",
      "epoch 1807  Loss = 0.364451\n",
      "epoch 1808  Loss = 0.364438\n",
      "epoch 1809  Loss = 0.364425\n",
      "epoch 1810  Loss = 0.364412\n",
      "epoch 1811  Loss = 0.364400\n",
      "epoch 1812  Loss = 0.364387\n",
      "epoch 1813  Loss = 0.364374\n",
      "epoch 1814  Loss = 0.364361\n",
      "epoch 1815  Loss = 0.364349\n",
      "epoch 1816  Loss = 0.364336\n",
      "epoch 1817  Loss = 0.364323\n",
      "epoch 1818  Loss = 0.364310\n",
      "epoch 1819  Loss = 0.364298\n",
      "epoch 1820  Loss = 0.364285\n",
      "epoch 1821  Loss = 0.364272\n",
      "epoch 1822  Loss = 0.364259\n",
      "epoch 1823  Loss = 0.364246\n",
      "epoch 1824  Loss = 0.364234\n",
      "epoch 1825  Loss = 0.364221\n",
      "epoch 1826  Loss = 0.364208\n",
      "epoch 1827  Loss = 0.364195\n",
      "epoch 1828  Loss = 0.364183\n",
      "epoch 1829  Loss = 0.364170\n",
      "epoch 1830  Loss = 0.364157\n",
      "epoch 1831  Loss = 0.364144\n",
      "epoch 1832  Loss = 0.364132\n",
      "epoch 1833  Loss = 0.364119\n",
      "epoch 1834  Loss = 0.364106\n",
      "epoch 1835  Loss = 0.364093\n",
      "epoch 1836  Loss = 0.364081\n",
      "epoch 1837  Loss = 0.364068\n",
      "epoch 1838  Loss = 0.364055\n",
      "epoch 1839  Loss = 0.364042\n",
      "epoch 1840  Loss = 0.364030\n",
      "epoch 1841  Loss = 0.364017\n",
      "epoch 1842  Loss = 0.364004\n",
      "epoch 1843  Loss = 0.363991\n",
      "epoch 1844  Loss = 0.363979\n",
      "epoch 1845  Loss = 0.363966\n",
      "epoch 1846  Loss = 0.363953\n",
      "epoch 1847  Loss = 0.363940\n",
      "epoch 1848  Loss = 0.363928\n",
      "epoch 1849  Loss = 0.363915\n",
      "epoch 1850  Loss = 0.363902\n",
      "epoch 1851  Loss = 0.363889\n",
      "epoch 1852  Loss = 0.363877\n",
      "epoch 1853  Loss = 0.363864\n",
      "epoch 1854  Loss = 0.363851\n",
      "epoch 1855  Loss = 0.363838\n",
      "epoch 1856  Loss = 0.363826\n",
      "epoch 1857  Loss = 0.363813\n",
      "epoch 1858  Loss = 0.363800\n",
      "epoch 1859  Loss = 0.363787\n",
      "epoch 1860  Loss = 0.363775\n",
      "epoch 1861  Loss = 0.363762\n",
      "epoch 1862  Loss = 0.363749\n",
      "epoch 1863  Loss = 0.363736\n",
      "epoch 1864  Loss = 0.363724\n",
      "epoch 1865  Loss = 0.363711\n",
      "epoch 1866  Loss = 0.363698\n",
      "epoch 1867  Loss = 0.363685\n",
      "epoch 1868  Loss = 0.363673\n",
      "epoch 1869  Loss = 0.363660\n",
      "epoch 1870  Loss = 0.363647\n",
      "epoch 1871  Loss = 0.363634\n",
      "epoch 1872  Loss = 0.363622\n",
      "epoch 1873  Loss = 0.363609\n",
      "epoch 1874  Loss = 0.363596\n",
      "epoch 1875  Loss = 0.363583\n",
      "epoch 1876  Loss = 0.363571\n",
      "epoch 1877  Loss = 0.363558\n",
      "epoch 1878  Loss = 0.363545\n",
      "epoch 1879  Loss = 0.363532\n",
      "epoch 1880  Loss = 0.363520\n",
      "epoch 1881  Loss = 0.363507\n",
      "epoch 1882  Loss = 0.363494\n",
      "epoch 1883  Loss = 0.363481\n",
      "epoch 1884  Loss = 0.363469\n",
      "epoch 1885  Loss = 0.363456\n",
      "epoch 1886  Loss = 0.363443\n",
      "epoch 1887  Loss = 0.363430\n",
      "epoch 1888  Loss = 0.363417\n",
      "epoch 1889  Loss = 0.363405\n",
      "epoch 1890  Loss = 0.363392\n",
      "epoch 1891  Loss = 0.363379\n",
      "epoch 1892  Loss = 0.363366\n",
      "epoch 1893  Loss = 0.363354\n",
      "epoch 1894  Loss = 0.363341\n",
      "epoch 1895  Loss = 0.363328\n",
      "epoch 1896  Loss = 0.363315\n",
      "epoch 1897  Loss = 0.363303\n",
      "epoch 1898  Loss = 0.363290\n",
      "epoch 1899  Loss = 0.363277\n",
      "epoch 1900  Loss = 0.363264\n",
      "epoch 1901  Loss = 0.363251\n",
      "epoch 1902  Loss = 0.363239\n",
      "epoch 1903  Loss = 0.363226\n",
      "epoch 1904  Loss = 0.363213\n",
      "epoch 1905  Loss = 0.363200\n",
      "epoch 1906  Loss = 0.363188\n",
      "epoch 1907  Loss = 0.363175\n",
      "epoch 1908  Loss = 0.363162\n",
      "epoch 1909  Loss = 0.363149\n",
      "epoch 1910  Loss = 0.363136\n",
      "epoch 1911  Loss = 0.363124\n",
      "epoch 1912  Loss = 0.363111\n",
      "epoch 1913  Loss = 0.363098\n",
      "epoch 1914  Loss = 0.363085\n",
      "epoch 1915  Loss = 0.363072\n",
      "epoch 1916  Loss = 0.363060\n",
      "epoch 1917  Loss = 0.363047\n",
      "epoch 1918  Loss = 0.363034\n",
      "epoch 1919  Loss = 0.363021\n",
      "epoch 1920  Loss = 0.363008\n",
      "epoch 1921  Loss = 0.362996\n",
      "epoch 1922  Loss = 0.362983\n",
      "epoch 1923  Loss = 0.362970\n",
      "epoch 1924  Loss = 0.362957\n",
      "epoch 1925  Loss = 0.362944\n",
      "epoch 1926  Loss = 0.362932\n",
      "epoch 1927  Loss = 0.362919\n",
      "epoch 1928  Loss = 0.362906\n",
      "epoch 1929  Loss = 0.362893\n",
      "epoch 1930  Loss = 0.362880\n",
      "epoch 1931  Loss = 0.362868\n",
      "epoch 1932  Loss = 0.362855\n",
      "epoch 1933  Loss = 0.362842\n",
      "epoch 1934  Loss = 0.362829\n",
      "epoch 1935  Loss = 0.362816\n",
      "epoch 1936  Loss = 0.362803\n",
      "epoch 1937  Loss = 0.362790\n",
      "epoch 1938  Loss = 0.362778\n",
      "epoch 1939  Loss = 0.362765\n",
      "epoch 1940  Loss = 0.362752\n",
      "epoch 1941  Loss = 0.362739\n",
      "epoch 1942  Loss = 0.362726\n",
      "epoch 1943  Loss = 0.362713\n",
      "epoch 1944  Loss = 0.362701\n",
      "epoch 1945  Loss = 0.362688\n",
      "epoch 1946  Loss = 0.362675\n",
      "epoch 1947  Loss = 0.362662\n",
      "epoch 1948  Loss = 0.362649\n",
      "epoch 1949  Loss = 0.362636\n",
      "epoch 1950  Loss = 0.362623\n",
      "epoch 1951  Loss = 0.362611\n",
      "epoch 1952  Loss = 0.362598\n",
      "epoch 1953  Loss = 0.362585\n",
      "epoch 1954  Loss = 0.362572\n",
      "epoch 1955  Loss = 0.362559\n",
      "epoch 1956  Loss = 0.362546\n",
      "epoch 1957  Loss = 0.362533\n",
      "epoch 1958  Loss = 0.362520\n",
      "epoch 1959  Loss = 0.362508\n",
      "epoch 1960  Loss = 0.362495\n",
      "epoch 1961  Loss = 0.362482\n",
      "epoch 1962  Loss = 0.362469\n",
      "epoch 1963  Loss = 0.362456\n",
      "epoch 1964  Loss = 0.362443\n",
      "epoch 1965  Loss = 0.362430\n",
      "epoch 1966  Loss = 0.362417\n",
      "epoch 1967  Loss = 0.362404\n",
      "epoch 1968  Loss = 0.362391\n",
      "epoch 1969  Loss = 0.362378\n",
      "epoch 1970  Loss = 0.362365\n",
      "epoch 1971  Loss = 0.362353\n",
      "epoch 1972  Loss = 0.362340\n",
      "epoch 1973  Loss = 0.362327\n",
      "epoch 1974  Loss = 0.362314\n",
      "epoch 1975  Loss = 0.362301\n",
      "epoch 1976  Loss = 0.362288\n",
      "epoch 1977  Loss = 0.362275\n",
      "epoch 1978  Loss = 0.362262\n",
      "epoch 1979  Loss = 0.362249\n",
      "epoch 1980  Loss = 0.362236\n",
      "epoch 1981  Loss = 0.362223\n",
      "epoch 1982  Loss = 0.362210\n",
      "epoch 1983  Loss = 0.362197\n",
      "epoch 1984  Loss = 0.362184\n",
      "epoch 1985  Loss = 0.362171\n",
      "epoch 1986  Loss = 0.362158\n",
      "epoch 1987  Loss = 0.362145\n",
      "epoch 1988  Loss = 0.362132\n",
      "epoch 1989  Loss = 0.362119\n",
      "epoch 1990  Loss = 0.362106\n",
      "epoch 1991  Loss = 0.362093\n",
      "epoch 1992  Loss = 0.362080\n",
      "epoch 1993  Loss = 0.362067\n",
      "epoch 1994  Loss = 0.362054\n",
      "epoch 1995  Loss = 0.362041\n",
      "epoch 1996  Loss = 0.362028\n",
      "epoch 1997  Loss = 0.362015\n",
      "epoch 1998  Loss = 0.362002\n",
      "epoch 1999  Loss = 0.361989\n",
      "epoch 2000  Loss = 0.361976\n",
      "epoch 2001  Loss = 0.361963\n",
      "epoch 2002  Loss = 0.361950\n",
      "epoch 2003  Loss = 0.361937\n",
      "epoch 2004  Loss = 0.361924\n",
      "epoch 2005  Loss = 0.361911\n",
      "epoch 2006  Loss = 0.361898\n",
      "epoch 2007  Loss = 0.361885\n",
      "epoch 2008  Loss = 0.361871\n",
      "epoch 2009  Loss = 0.361858\n",
      "epoch 2010  Loss = 0.361845\n",
      "epoch 2011  Loss = 0.361832\n",
      "epoch 2012  Loss = 0.361819\n",
      "epoch 2013  Loss = 0.361806\n",
      "epoch 2014  Loss = 0.361793\n",
      "epoch 2015  Loss = 0.361780\n",
      "epoch 2016  Loss = 0.361767\n",
      "epoch 2017  Loss = 0.361753\n",
      "epoch 2018  Loss = 0.361740\n",
      "epoch 2019  Loss = 0.361727\n",
      "epoch 2020  Loss = 0.361714\n",
      "epoch 2021  Loss = 0.361701\n",
      "epoch 2022  Loss = 0.361688\n",
      "epoch 2023  Loss = 0.361675\n",
      "epoch 2024  Loss = 0.361661\n",
      "epoch 2025  Loss = 0.361648\n",
      "epoch 2026  Loss = 0.361635\n",
      "epoch 2027  Loss = 0.361622\n",
      "epoch 2028  Loss = 0.361609\n",
      "epoch 2029  Loss = 0.361596\n",
      "epoch 2030  Loss = 0.361582\n",
      "epoch 2031  Loss = 0.361569\n",
      "epoch 2032  Loss = 0.361556\n",
      "epoch 2033  Loss = 0.361543\n",
      "epoch 2034  Loss = 0.361529\n",
      "epoch 2035  Loss = 0.361516\n",
      "epoch 2036  Loss = 0.361503\n",
      "epoch 2037  Loss = 0.361490\n",
      "epoch 2038  Loss = 0.361477\n",
      "epoch 2039  Loss = 0.361463\n",
      "epoch 2040  Loss = 0.361450\n",
      "epoch 2041  Loss = 0.361437\n",
      "epoch 2042  Loss = 0.361423\n",
      "epoch 2043  Loss = 0.361410\n",
      "epoch 2044  Loss = 0.361397\n",
      "epoch 2045  Loss = 0.361384\n",
      "epoch 2046  Loss = 0.361370\n",
      "epoch 2047  Loss = 0.361357\n",
      "epoch 2048  Loss = 0.361344\n",
      "epoch 2049  Loss = 0.361330\n",
      "epoch 2050  Loss = 0.361317\n",
      "epoch 2051  Loss = 0.361304\n",
      "epoch 2052  Loss = 0.361290\n",
      "epoch 2053  Loss = 0.361277\n",
      "epoch 2054  Loss = 0.361264\n",
      "epoch 2055  Loss = 0.361250\n",
      "epoch 2056  Loss = 0.361237\n",
      "epoch 2057  Loss = 0.361224\n",
      "epoch 2058  Loss = 0.361210\n",
      "epoch 2059  Loss = 0.361197\n",
      "epoch 2060  Loss = 0.361183\n",
      "epoch 2061  Loss = 0.361170\n",
      "epoch 2062  Loss = 0.361157\n",
      "epoch 2063  Loss = 0.361143\n",
      "epoch 2064  Loss = 0.361130\n",
      "epoch 2065  Loss = 0.361116\n",
      "epoch 2066  Loss = 0.361103\n",
      "epoch 2067  Loss = 0.361089\n",
      "epoch 2068  Loss = 0.361076\n",
      "epoch 2069  Loss = 0.361062\n",
      "epoch 2070  Loss = 0.361049\n",
      "epoch 2071  Loss = 0.361035\n",
      "epoch 2072  Loss = 0.361022\n",
      "epoch 2073  Loss = 0.361008\n",
      "epoch 2074  Loss = 0.360995\n",
      "epoch 2075  Loss = 0.360981\n",
      "epoch 2076  Loss = 0.360968\n",
      "epoch 2077  Loss = 0.360954\n",
      "epoch 2078  Loss = 0.360941\n",
      "epoch 2079  Loss = 0.360927\n",
      "epoch 2080  Loss = 0.360914\n",
      "epoch 2081  Loss = 0.360900\n",
      "epoch 2082  Loss = 0.360886\n",
      "epoch 2083  Loss = 0.360873\n",
      "epoch 2084  Loss = 0.360859\n",
      "epoch 2085  Loss = 0.360846\n",
      "epoch 2086  Loss = 0.360832\n",
      "epoch 2087  Loss = 0.360818\n",
      "epoch 2088  Loss = 0.360805\n",
      "epoch 2089  Loss = 0.360791\n",
      "epoch 2090  Loss = 0.360777\n",
      "epoch 2091  Loss = 0.360764\n",
      "epoch 2092  Loss = 0.360750\n",
      "epoch 2093  Loss = 0.360736\n",
      "epoch 2094  Loss = 0.360723\n",
      "epoch 2095  Loss = 0.360709\n",
      "epoch 2096  Loss = 0.360695\n",
      "epoch 2097  Loss = 0.360681\n",
      "epoch 2098  Loss = 0.360668\n",
      "epoch 2099  Loss = 0.360654\n",
      "epoch 2100  Loss = 0.360640\n",
      "epoch 2101  Loss = 0.360626\n",
      "epoch 2102  Loss = 0.360613\n",
      "epoch 2103  Loss = 0.360599\n",
      "epoch 2104  Loss = 0.360585\n",
      "epoch 2105  Loss = 0.360571\n",
      "epoch 2106  Loss = 0.360557\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2107  Loss = 0.360544\n",
      "epoch 2108  Loss = 0.360530\n",
      "epoch 2109  Loss = 0.360516\n",
      "epoch 2110  Loss = 0.360502\n",
      "epoch 2111  Loss = 0.360488\n",
      "epoch 2112  Loss = 0.360474\n",
      "epoch 2113  Loss = 0.360460\n",
      "epoch 2114  Loss = 0.360446\n",
      "epoch 2115  Loss = 0.360432\n",
      "epoch 2116  Loss = 0.360418\n",
      "epoch 2117  Loss = 0.360404\n",
      "epoch 2118  Loss = 0.360390\n",
      "epoch 2119  Loss = 0.360376\n",
      "epoch 2120  Loss = 0.360362\n",
      "epoch 2121  Loss = 0.360348\n",
      "epoch 2122  Loss = 0.360334\n",
      "epoch 2123  Loss = 0.360320\n",
      "epoch 2124  Loss = 0.360306\n",
      "epoch 2125  Loss = 0.360292\n",
      "epoch 2126  Loss = 0.360278\n",
      "epoch 2127  Loss = 0.360264\n",
      "epoch 2128  Loss = 0.360250\n",
      "epoch 2129  Loss = 0.360236\n",
      "epoch 2130  Loss = 0.360222\n",
      "epoch 2131  Loss = 0.360208\n",
      "epoch 2132  Loss = 0.360193\n",
      "epoch 2133  Loss = 0.360179\n",
      "epoch 2134  Loss = 0.360165\n",
      "epoch 2135  Loss = 0.360151\n",
      "epoch 2136  Loss = 0.360137\n",
      "epoch 2137  Loss = 0.360122\n",
      "epoch 2138  Loss = 0.360108\n",
      "epoch 2139  Loss = 0.360094\n",
      "epoch 2140  Loss = 0.360080\n",
      "epoch 2141  Loss = 0.360065\n",
      "epoch 2142  Loss = 0.360051\n",
      "epoch 2143  Loss = 0.360037\n",
      "epoch 2144  Loss = 0.360022\n",
      "epoch 2145  Loss = 0.360008\n",
      "epoch 2146  Loss = 0.359994\n",
      "epoch 2147  Loss = 0.359979\n",
      "epoch 2148  Loss = 0.359965\n",
      "epoch 2149  Loss = 0.359951\n",
      "epoch 2150  Loss = 0.359936\n",
      "epoch 2151  Loss = 0.359922\n",
      "epoch 2152  Loss = 0.359907\n",
      "epoch 2153  Loss = 0.359893\n",
      "epoch 2154  Loss = 0.359878\n",
      "epoch 2155  Loss = 0.359864\n",
      "epoch 2156  Loss = 0.359849\n",
      "epoch 2157  Loss = 0.359835\n",
      "epoch 2158  Loss = 0.359820\n",
      "epoch 2159  Loss = 0.359805\n",
      "epoch 2160  Loss = 0.359791\n",
      "epoch 2161  Loss = 0.359776\n",
      "epoch 2162  Loss = 0.359762\n",
      "epoch 2163  Loss = 0.359747\n",
      "epoch 2164  Loss = 0.359732\n",
      "epoch 2165  Loss = 0.359718\n",
      "epoch 2166  Loss = 0.359703\n",
      "epoch 2167  Loss = 0.359688\n",
      "epoch 2168  Loss = 0.359673\n",
      "epoch 2169  Loss = 0.359659\n",
      "epoch 2170  Loss = 0.359644\n",
      "epoch 2171  Loss = 0.359629\n",
      "epoch 2172  Loss = 0.359614\n",
      "epoch 2173  Loss = 0.359599\n",
      "epoch 2174  Loss = 0.359585\n",
      "epoch 2175  Loss = 0.359570\n",
      "epoch 2176  Loss = 0.359555\n",
      "epoch 2177  Loss = 0.359540\n",
      "epoch 2178  Loss = 0.359525\n",
      "epoch 2179  Loss = 0.359510\n",
      "epoch 2180  Loss = 0.359495\n",
      "epoch 2181  Loss = 0.359480\n",
      "epoch 2182  Loss = 0.359465\n",
      "epoch 2183  Loss = 0.359450\n",
      "epoch 2184  Loss = 0.359435\n",
      "epoch 2185  Loss = 0.359420\n",
      "epoch 2186  Loss = 0.359405\n",
      "epoch 2187  Loss = 0.359389\n",
      "epoch 2188  Loss = 0.359374\n",
      "epoch 2189  Loss = 0.359359\n",
      "epoch 2190  Loss = 0.359344\n",
      "epoch 2191  Loss = 0.359329\n",
      "epoch 2192  Loss = 0.359313\n",
      "epoch 2193  Loss = 0.359298\n",
      "epoch 2194  Loss = 0.359283\n",
      "epoch 2195  Loss = 0.359267\n",
      "epoch 2196  Loss = 0.359252\n",
      "epoch 2197  Loss = 0.359237\n",
      "epoch 2198  Loss = 0.359221\n",
      "epoch 2199  Loss = 0.359206\n",
      "epoch 2200  Loss = 0.359190\n",
      "epoch 2201  Loss = 0.359175\n",
      "epoch 2202  Loss = 0.359159\n",
      "epoch 2203  Loss = 0.359144\n",
      "epoch 2204  Loss = 0.359128\n",
      "epoch 2205  Loss = 0.359113\n",
      "epoch 2206  Loss = 0.359097\n",
      "epoch 2207  Loss = 0.359081\n",
      "epoch 2208  Loss = 0.359066\n",
      "epoch 2209  Loss = 0.359050\n",
      "epoch 2210  Loss = 0.359034\n",
      "epoch 2211  Loss = 0.359019\n",
      "epoch 2212  Loss = 0.359003\n",
      "epoch 2213  Loss = 0.358987\n",
      "epoch 2214  Loss = 0.358971\n",
      "epoch 2215  Loss = 0.358955\n",
      "epoch 2216  Loss = 0.358939\n",
      "epoch 2217  Loss = 0.358923\n",
      "epoch 2218  Loss = 0.358907\n",
      "epoch 2219  Loss = 0.358891\n",
      "epoch 2220  Loss = 0.358875\n",
      "epoch 2221  Loss = 0.358859\n",
      "epoch 2222  Loss = 0.358843\n",
      "epoch 2223  Loss = 0.358827\n",
      "epoch 2224  Loss = 0.358811\n",
      "epoch 2225  Loss = 0.358795\n",
      "epoch 2226  Loss = 0.358779\n",
      "epoch 2227  Loss = 0.358762\n",
      "epoch 2228  Loss = 0.358746\n",
      "epoch 2229  Loss = 0.358730\n",
      "epoch 2230  Loss = 0.358713\n",
      "epoch 2231  Loss = 0.358697\n",
      "epoch 2232  Loss = 0.358681\n",
      "epoch 2233  Loss = 0.358664\n",
      "epoch 2234  Loss = 0.358648\n",
      "epoch 2235  Loss = 0.358631\n",
      "epoch 2236  Loss = 0.358615\n",
      "epoch 2237  Loss = 0.358598\n",
      "epoch 2238  Loss = 0.358581\n",
      "epoch 2239  Loss = 0.358565\n",
      "epoch 2240  Loss = 0.358548\n",
      "epoch 2241  Loss = 0.358531\n",
      "epoch 2242  Loss = 0.358514\n",
      "epoch 2243  Loss = 0.358498\n",
      "epoch 2244  Loss = 0.358481\n",
      "epoch 2245  Loss = 0.358464\n",
      "epoch 2246  Loss = 0.358447\n",
      "epoch 2247  Loss = 0.358430\n",
      "epoch 2248  Loss = 0.358413\n",
      "epoch 2249  Loss = 0.358396\n",
      "epoch 2250  Loss = 0.358379\n",
      "epoch 2251  Loss = 0.358362\n",
      "epoch 2252  Loss = 0.358345\n",
      "epoch 2253  Loss = 0.358327\n",
      "epoch 2254  Loss = 0.358310\n",
      "epoch 2255  Loss = 0.358293\n",
      "epoch 2256  Loss = 0.358276\n",
      "epoch 2257  Loss = 0.358258\n",
      "epoch 2258  Loss = 0.358241\n",
      "epoch 2259  Loss = 0.358223\n",
      "epoch 2260  Loss = 0.358206\n",
      "epoch 2261  Loss = 0.358188\n",
      "epoch 2262  Loss = 0.358171\n",
      "epoch 2263  Loss = 0.358153\n",
      "epoch 2264  Loss = 0.358135\n",
      "epoch 2265  Loss = 0.358118\n",
      "epoch 2266  Loss = 0.358100\n",
      "epoch 2267  Loss = 0.358082\n",
      "epoch 2268  Loss = 0.358064\n",
      "epoch 2269  Loss = 0.358046\n",
      "epoch 2270  Loss = 0.358028\n",
      "epoch 2271  Loss = 0.358010\n",
      "epoch 2272  Loss = 0.357992\n",
      "epoch 2273  Loss = 0.357974\n",
      "epoch 2274  Loss = 0.357956\n",
      "epoch 2275  Loss = 0.357938\n",
      "epoch 2276  Loss = 0.357920\n",
      "epoch 2277  Loss = 0.357901\n",
      "epoch 2278  Loss = 0.357883\n",
      "epoch 2279  Loss = 0.357865\n",
      "epoch 2280  Loss = 0.357846\n",
      "epoch 2281  Loss = 0.357828\n",
      "epoch 2282  Loss = 0.357809\n",
      "epoch 2283  Loss = 0.357791\n",
      "epoch 2284  Loss = 0.357772\n",
      "epoch 2285  Loss = 0.357753\n",
      "epoch 2286  Loss = 0.357735\n",
      "epoch 2287  Loss = 0.357716\n",
      "epoch 2288  Loss = 0.357697\n",
      "epoch 2289  Loss = 0.357678\n",
      "epoch 2290  Loss = 0.357659\n",
      "epoch 2291  Loss = 0.357640\n",
      "epoch 2292  Loss = 0.357621\n",
      "epoch 2293  Loss = 0.357602\n",
      "epoch 2294  Loss = 0.357583\n",
      "epoch 2295  Loss = 0.357564\n",
      "epoch 2296  Loss = 0.357545\n",
      "epoch 2297  Loss = 0.357526\n",
      "epoch 2298  Loss = 0.357506\n",
      "epoch 2299  Loss = 0.357487\n",
      "epoch 2300  Loss = 0.357468\n",
      "epoch 2301  Loss = 0.357448\n",
      "epoch 2302  Loss = 0.357429\n",
      "epoch 2303  Loss = 0.357409\n",
      "epoch 2304  Loss = 0.357389\n",
      "epoch 2305  Loss = 0.357370\n",
      "epoch 2306  Loss = 0.357350\n",
      "epoch 2307  Loss = 0.357330\n",
      "epoch 2308  Loss = 0.357311\n",
      "epoch 2309  Loss = 0.357291\n",
      "epoch 2310  Loss = 0.357271\n",
      "epoch 2311  Loss = 0.357251\n",
      "epoch 2312  Loss = 0.357231\n",
      "epoch 2313  Loss = 0.357211\n",
      "epoch 2314  Loss = 0.357191\n",
      "epoch 2315  Loss = 0.357170\n",
      "epoch 2316  Loss = 0.357150\n",
      "epoch 2317  Loss = 0.357130\n",
      "epoch 2318  Loss = 0.357110\n",
      "epoch 2319  Loss = 0.357089\n",
      "epoch 2320  Loss = 0.357069\n",
      "epoch 2321  Loss = 0.357048\n",
      "epoch 2322  Loss = 0.357028\n",
      "epoch 2323  Loss = 0.357007\n",
      "epoch 2324  Loss = 0.356987\n",
      "epoch 2325  Loss = 0.356966\n",
      "epoch 2326  Loss = 0.356945\n",
      "epoch 2327  Loss = 0.356925\n",
      "epoch 2328  Loss = 0.356904\n",
      "epoch 2329  Loss = 0.356883\n",
      "epoch 2330  Loss = 0.356862\n",
      "epoch 2331  Loss = 0.356841\n",
      "epoch 2332  Loss = 0.356820\n",
      "epoch 2333  Loss = 0.356799\n",
      "epoch 2334  Loss = 0.356778\n",
      "epoch 2335  Loss = 0.356757\n",
      "epoch 2336  Loss = 0.356736\n",
      "epoch 2337  Loss = 0.356715\n",
      "epoch 2338  Loss = 0.356693\n",
      "epoch 2339  Loss = 0.356672\n",
      "epoch 2340  Loss = 0.356651\n",
      "epoch 2341  Loss = 0.356629\n",
      "epoch 2342  Loss = 0.356608\n",
      "epoch 2343  Loss = 0.356586\n",
      "epoch 2344  Loss = 0.356565\n",
      "epoch 2345  Loss = 0.356543\n",
      "epoch 2346  Loss = 0.356522\n",
      "epoch 2347  Loss = 0.356500\n",
      "epoch 2348  Loss = 0.356478\n",
      "epoch 2349  Loss = 0.356457\n",
      "epoch 2350  Loss = 0.356435\n",
      "epoch 2351  Loss = 0.356413\n",
      "epoch 2352  Loss = 0.356391\n",
      "epoch 2353  Loss = 0.356369\n",
      "epoch 2354  Loss = 0.356347\n",
      "epoch 2355  Loss = 0.356326\n",
      "epoch 2356  Loss = 0.356304\n",
      "epoch 2357  Loss = 0.356282\n",
      "epoch 2358  Loss = 0.356259\n",
      "epoch 2359  Loss = 0.356237\n",
      "epoch 2360  Loss = 0.356215\n",
      "epoch 2361  Loss = 0.356193\n",
      "epoch 2362  Loss = 0.356171\n",
      "epoch 2363  Loss = 0.356149\n",
      "epoch 2364  Loss = 0.356126\n",
      "epoch 2365  Loss = 0.356104\n",
      "epoch 2366  Loss = 0.356082\n",
      "epoch 2367  Loss = 0.356060\n",
      "epoch 2368  Loss = 0.356037\n",
      "epoch 2369  Loss = 0.356015\n",
      "epoch 2370  Loss = 0.355992\n",
      "epoch 2371  Loss = 0.355970\n",
      "epoch 2372  Loss = 0.355947\n",
      "epoch 2373  Loss = 0.355925\n",
      "epoch 2374  Loss = 0.355902\n",
      "epoch 2375  Loss = 0.355880\n",
      "epoch 2376  Loss = 0.355857\n",
      "epoch 2377  Loss = 0.355834\n",
      "epoch 2378  Loss = 0.355812\n",
      "epoch 2379  Loss = 0.355789\n",
      "epoch 2380  Loss = 0.355766\n",
      "epoch 2381  Loss = 0.355744\n",
      "epoch 2382  Loss = 0.355721\n",
      "epoch 2383  Loss = 0.355698\n",
      "epoch 2384  Loss = 0.355675\n",
      "epoch 2385  Loss = 0.355652\n",
      "epoch 2386  Loss = 0.355630\n",
      "epoch 2387  Loss = 0.355607\n",
      "epoch 2388  Loss = 0.355584\n",
      "epoch 2389  Loss = 0.355561\n",
      "epoch 2390  Loss = 0.355538\n",
      "epoch 2391  Loss = 0.355515\n",
      "epoch 2392  Loss = 0.355492\n",
      "epoch 2393  Loss = 0.355469\n",
      "epoch 2394  Loss = 0.355446\n",
      "epoch 2395  Loss = 0.355423\n",
      "epoch 2396  Loss = 0.355400\n",
      "epoch 2397  Loss = 0.355377\n",
      "epoch 2398  Loss = 0.355354\n",
      "epoch 2399  Loss = 0.355331\n",
      "epoch 2400  Loss = 0.355308\n",
      "epoch 2401  Loss = 0.355285\n",
      "epoch 2402  Loss = 0.355262\n",
      "epoch 2403  Loss = 0.355239\n",
      "epoch 2404  Loss = 0.355215\n",
      "epoch 2405  Loss = 0.355192\n",
      "epoch 2406  Loss = 0.355169\n",
      "epoch 2407  Loss = 0.355146\n",
      "epoch 2408  Loss = 0.355123\n",
      "epoch 2409  Loss = 0.355099\n",
      "epoch 2410  Loss = 0.355076\n",
      "epoch 2411  Loss = 0.355053\n",
      "epoch 2412  Loss = 0.355030\n",
      "epoch 2413  Loss = 0.355007\n",
      "epoch 2414  Loss = 0.354983\n",
      "epoch 2415  Loss = 0.354960\n",
      "epoch 2416  Loss = 0.354937\n",
      "epoch 2417  Loss = 0.354914\n",
      "epoch 2418  Loss = 0.354890\n",
      "epoch 2419  Loss = 0.354867\n",
      "epoch 2420  Loss = 0.354844\n",
      "epoch 2421  Loss = 0.354820\n",
      "epoch 2422  Loss = 0.354797\n",
      "epoch 2423  Loss = 0.354774\n",
      "epoch 2424  Loss = 0.354750\n",
      "epoch 2425  Loss = 0.354727\n",
      "epoch 2426  Loss = 0.354704\n",
      "epoch 2427  Loss = 0.354680\n",
      "epoch 2428  Loss = 0.354657\n",
      "epoch 2429  Loss = 0.354634\n",
      "epoch 2430  Loss = 0.354610\n",
      "epoch 2431  Loss = 0.354587\n",
      "epoch 2432  Loss = 0.354563\n",
      "epoch 2433  Loss = 0.354540\n",
      "epoch 2434  Loss = 0.354517\n",
      "epoch 2435  Loss = 0.354493\n",
      "epoch 2436  Loss = 0.354470\n",
      "epoch 2437  Loss = 0.354446\n",
      "epoch 2438  Loss = 0.354423\n",
      "epoch 2439  Loss = 0.354400\n",
      "epoch 2440  Loss = 0.354376\n",
      "epoch 2441  Loss = 0.354353\n",
      "epoch 2442  Loss = 0.354329\n",
      "epoch 2443  Loss = 0.354306\n",
      "epoch 2444  Loss = 0.354283\n",
      "epoch 2445  Loss = 0.354259\n",
      "epoch 2446  Loss = 0.354236\n",
      "epoch 2447  Loss = 0.354212\n",
      "epoch 2448  Loss = 0.354189\n",
      "epoch 2449  Loss = 0.354165\n",
      "epoch 2450  Loss = 0.354142\n",
      "epoch 2451  Loss = 0.354119\n",
      "epoch 2452  Loss = 0.354095\n",
      "epoch 2453  Loss = 0.354072\n",
      "epoch 2454  Loss = 0.354048\n",
      "epoch 2455  Loss = 0.354025\n",
      "epoch 2456  Loss = 0.354001\n",
      "epoch 2457  Loss = 0.353978\n",
      "epoch 2458  Loss = 0.353955\n",
      "epoch 2459  Loss = 0.353931\n",
      "epoch 2460  Loss = 0.353908\n",
      "epoch 2461  Loss = 0.353884\n",
      "epoch 2462  Loss = 0.353861\n",
      "epoch 2463  Loss = 0.353838\n",
      "epoch 2464  Loss = 0.353814\n",
      "epoch 2465  Loss = 0.353791\n",
      "epoch 2466  Loss = 0.353767\n",
      "epoch 2467  Loss = 0.353744\n",
      "epoch 2468  Loss = 0.353720\n",
      "epoch 2469  Loss = 0.353697\n",
      "epoch 2470  Loss = 0.353674\n",
      "epoch 2471  Loss = 0.353650\n",
      "epoch 2472  Loss = 0.353627\n",
      "epoch 2473  Loss = 0.353603\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2474  Loss = 0.353580\n",
      "epoch 2475  Loss = 0.353557\n",
      "epoch 2476  Loss = 0.353533\n",
      "epoch 2477  Loss = 0.353510\n",
      "epoch 2478  Loss = 0.353487\n",
      "epoch 2479  Loss = 0.353463\n",
      "epoch 2480  Loss = 0.353440\n",
      "epoch 2481  Loss = 0.353416\n",
      "epoch 2482  Loss = 0.353393\n",
      "epoch 2483  Loss = 0.353370\n",
      "epoch 2484  Loss = 0.353346\n",
      "epoch 2485  Loss = 0.353323\n",
      "epoch 2486  Loss = 0.353300\n",
      "epoch 2487  Loss = 0.353276\n",
      "epoch 2488  Loss = 0.353253\n",
      "epoch 2489  Loss = 0.353230\n",
      "epoch 2490  Loss = 0.353206\n",
      "epoch 2491  Loss = 0.353183\n",
      "epoch 2492  Loss = 0.353160\n",
      "epoch 2493  Loss = 0.353136\n",
      "epoch 2494  Loss = 0.353113\n",
      "epoch 2495  Loss = 0.353090\n",
      "epoch 2496  Loss = 0.353066\n",
      "epoch 2497  Loss = 0.353043\n",
      "epoch 2498  Loss = 0.353020\n",
      "epoch 2499  Loss = 0.352997\n",
      "epoch 2500  Loss = 0.352973\n",
      "epoch 2501  Loss = 0.352950\n",
      "epoch 2502  Loss = 0.352927\n",
      "epoch 2503  Loss = 0.352903\n",
      "epoch 2504  Loss = 0.352880\n",
      "epoch 2505  Loss = 0.352857\n",
      "epoch 2506  Loss = 0.352834\n",
      "epoch 2507  Loss = 0.352811\n",
      "epoch 2508  Loss = 0.352787\n",
      "epoch 2509  Loss = 0.352764\n",
      "epoch 2510  Loss = 0.352741\n",
      "epoch 2511  Loss = 0.352718\n",
      "epoch 2512  Loss = 0.352694\n",
      "epoch 2513  Loss = 0.352671\n",
      "epoch 2514  Loss = 0.352648\n",
      "epoch 2515  Loss = 0.352625\n",
      "epoch 2516  Loss = 0.352602\n",
      "epoch 2517  Loss = 0.352579\n",
      "epoch 2518  Loss = 0.352555\n",
      "epoch 2519  Loss = 0.352532\n",
      "epoch 2520  Loss = 0.352509\n",
      "epoch 2521  Loss = 0.352486\n",
      "epoch 2522  Loss = 0.352463\n",
      "epoch 2523  Loss = 0.352440\n",
      "epoch 2524  Loss = 0.352417\n",
      "epoch 2525  Loss = 0.352394\n",
      "epoch 2526  Loss = 0.352370\n",
      "epoch 2527  Loss = 0.352347\n",
      "epoch 2528  Loss = 0.352324\n",
      "epoch 2529  Loss = 0.352301\n",
      "epoch 2530  Loss = 0.352278\n",
      "epoch 2531  Loss = 0.352255\n",
      "epoch 2532  Loss = 0.352232\n",
      "epoch 2533  Loss = 0.352209\n",
      "epoch 2534  Loss = 0.352186\n",
      "epoch 2535  Loss = 0.352163\n",
      "epoch 2536  Loss = 0.352140\n",
      "epoch 2537  Loss = 0.352117\n",
      "epoch 2538  Loss = 0.352094\n",
      "epoch 2539  Loss = 0.352071\n",
      "epoch 2540  Loss = 0.352048\n",
      "epoch 2541  Loss = 0.352025\n",
      "epoch 2542  Loss = 0.352002\n",
      "epoch 2543  Loss = 0.351979\n",
      "epoch 2544  Loss = 0.351956\n",
      "epoch 2545  Loss = 0.351933\n",
      "epoch 2546  Loss = 0.351910\n",
      "epoch 2547  Loss = 0.351887\n",
      "epoch 2548  Loss = 0.351865\n",
      "epoch 2549  Loss = 0.351842\n",
      "epoch 2550  Loss = 0.351819\n",
      "epoch 2551  Loss = 0.351796\n",
      "epoch 2552  Loss = 0.351773\n",
      "epoch 2553  Loss = 0.351750\n",
      "epoch 2554  Loss = 0.351727\n",
      "epoch 2555  Loss = 0.351704\n",
      "epoch 2556  Loss = 0.351682\n",
      "epoch 2557  Loss = 0.351659\n",
      "epoch 2558  Loss = 0.351636\n",
      "epoch 2559  Loss = 0.351613\n",
      "epoch 2560  Loss = 0.351590\n",
      "epoch 2561  Loss = 0.351568\n",
      "epoch 2562  Loss = 0.351545\n",
      "epoch 2563  Loss = 0.351522\n",
      "epoch 2564  Loss = 0.351499\n",
      "epoch 2565  Loss = 0.351477\n",
      "epoch 2566  Loss = 0.351454\n",
      "epoch 2567  Loss = 0.351431\n",
      "epoch 2568  Loss = 0.351409\n",
      "epoch 2569  Loss = 0.351386\n",
      "epoch 2570  Loss = 0.351363\n",
      "epoch 2571  Loss = 0.351341\n",
      "epoch 2572  Loss = 0.351318\n",
      "epoch 2573  Loss = 0.351295\n",
      "epoch 2574  Loss = 0.351273\n",
      "epoch 2575  Loss = 0.351250\n",
      "epoch 2576  Loss = 0.351228\n",
      "epoch 2577  Loss = 0.351205\n",
      "epoch 2578  Loss = 0.351182\n",
      "epoch 2579  Loss = 0.351160\n",
      "epoch 2580  Loss = 0.351137\n",
      "epoch 2581  Loss = 0.351115\n",
      "epoch 2582  Loss = 0.351092\n",
      "epoch 2583  Loss = 0.351070\n",
      "epoch 2584  Loss = 0.351047\n",
      "epoch 2585  Loss = 0.351025\n",
      "epoch 2586  Loss = 0.351002\n",
      "epoch 2587  Loss = 0.350980\n",
      "epoch 2588  Loss = 0.350957\n",
      "epoch 2589  Loss = 0.350935\n",
      "epoch 2590  Loss = 0.350912\n",
      "epoch 2591  Loss = 0.350890\n",
      "epoch 2592  Loss = 0.350868\n",
      "epoch 2593  Loss = 0.350845\n",
      "epoch 2594  Loss = 0.350823\n",
      "epoch 2595  Loss = 0.350800\n",
      "epoch 2596  Loss = 0.350778\n",
      "epoch 2597  Loss = 0.350756\n",
      "epoch 2598  Loss = 0.350733\n",
      "epoch 2599  Loss = 0.350711\n",
      "epoch 2600  Loss = 0.350689\n",
      "epoch 2601  Loss = 0.350666\n",
      "epoch 2602  Loss = 0.350644\n",
      "epoch 2603  Loss = 0.350622\n",
      "epoch 2604  Loss = 0.350600\n",
      "epoch 2605  Loss = 0.350577\n",
      "epoch 2606  Loss = 0.350555\n",
      "epoch 2607  Loss = 0.350533\n",
      "epoch 2608  Loss = 0.350511\n",
      "epoch 2609  Loss = 0.350489\n",
      "epoch 2610  Loss = 0.350466\n",
      "epoch 2611  Loss = 0.350444\n",
      "epoch 2612  Loss = 0.350422\n",
      "epoch 2613  Loss = 0.350400\n",
      "epoch 2614  Loss = 0.350378\n",
      "epoch 2615  Loss = 0.350356\n",
      "epoch 2616  Loss = 0.350334\n",
      "epoch 2617  Loss = 0.350311\n",
      "epoch 2618  Loss = 0.350289\n",
      "epoch 2619  Loss = 0.350267\n",
      "epoch 2620  Loss = 0.350245\n",
      "epoch 2621  Loss = 0.350223\n",
      "epoch 2622  Loss = 0.350201\n",
      "epoch 2623  Loss = 0.350179\n",
      "epoch 2624  Loss = 0.350157\n",
      "epoch 2625  Loss = 0.350135\n",
      "epoch 2626  Loss = 0.350113\n",
      "epoch 2627  Loss = 0.350091\n",
      "epoch 2628  Loss = 0.350069\n",
      "epoch 2629  Loss = 0.350047\n",
      "epoch 2630  Loss = 0.350026\n",
      "epoch 2631  Loss = 0.350004\n",
      "epoch 2632  Loss = 0.349982\n",
      "epoch 2633  Loss = 0.349960\n",
      "epoch 2634  Loss = 0.349938\n",
      "epoch 2635  Loss = 0.349916\n",
      "epoch 2636  Loss = 0.349894\n",
      "epoch 2637  Loss = 0.349873\n",
      "epoch 2638  Loss = 0.349851\n",
      "epoch 2639  Loss = 0.349829\n",
      "epoch 2640  Loss = 0.349807\n",
      "epoch 2641  Loss = 0.349786\n",
      "epoch 2642  Loss = 0.349764\n",
      "epoch 2643  Loss = 0.349742\n",
      "epoch 2644  Loss = 0.349720\n",
      "epoch 2645  Loss = 0.349699\n",
      "epoch 2646  Loss = 0.349677\n",
      "epoch 2647  Loss = 0.349655\n",
      "epoch 2648  Loss = 0.349634\n",
      "epoch 2649  Loss = 0.349612\n",
      "epoch 2650  Loss = 0.349590\n",
      "epoch 2651  Loss = 0.349569\n",
      "epoch 2652  Loss = 0.349547\n",
      "epoch 2653  Loss = 0.349525\n",
      "epoch 2654  Loss = 0.349504\n",
      "epoch 2655  Loss = 0.349482\n",
      "epoch 2656  Loss = 0.349461\n",
      "epoch 2657  Loss = 0.349439\n",
      "epoch 2658  Loss = 0.349418\n",
      "epoch 2659  Loss = 0.349396\n",
      "epoch 2660  Loss = 0.349375\n",
      "epoch 2661  Loss = 0.349353\n",
      "epoch 2662  Loss = 0.349332\n",
      "epoch 2663  Loss = 0.349310\n",
      "epoch 2664  Loss = 0.349289\n",
      "epoch 2665  Loss = 0.349267\n",
      "epoch 2666  Loss = 0.349246\n",
      "epoch 2667  Loss = 0.349225\n",
      "epoch 2668  Loss = 0.349203\n",
      "epoch 2669  Loss = 0.349182\n",
      "epoch 2670  Loss = 0.349160\n",
      "epoch 2671  Loss = 0.349139\n",
      "epoch 2672  Loss = 0.349118\n",
      "epoch 2673  Loss = 0.349096\n",
      "epoch 2674  Loss = 0.349075\n",
      "epoch 2675  Loss = 0.349054\n",
      "epoch 2676  Loss = 0.349033\n",
      "epoch 2677  Loss = 0.349011\n",
      "epoch 2678  Loss = 0.348990\n",
      "epoch 2679  Loss = 0.348969\n",
      "epoch 2680  Loss = 0.348948\n",
      "epoch 2681  Loss = 0.348926\n",
      "epoch 2682  Loss = 0.348905\n",
      "epoch 2683  Loss = 0.348884\n",
      "epoch 2684  Loss = 0.348863\n",
      "epoch 2685  Loss = 0.348842\n",
      "epoch 2686  Loss = 0.348821\n",
      "epoch 2687  Loss = 0.348799\n",
      "epoch 2688  Loss = 0.348778\n",
      "epoch 2689  Loss = 0.348757\n",
      "epoch 2690  Loss = 0.348736\n",
      "epoch 2691  Loss = 0.348715\n",
      "epoch 2692  Loss = 0.348694\n",
      "epoch 2693  Loss = 0.348673\n",
      "epoch 2694  Loss = 0.348652\n",
      "epoch 2695  Loss = 0.348631\n",
      "epoch 2696  Loss = 0.348610\n",
      "epoch 2697  Loss = 0.348589\n",
      "epoch 2698  Loss = 0.348568\n",
      "epoch 2699  Loss = 0.348547\n",
      "epoch 2700  Loss = 0.348526\n",
      "epoch 2701  Loss = 0.348505\n",
      "epoch 2702  Loss = 0.348484\n",
      "epoch 2703  Loss = 0.348464\n",
      "epoch 2704  Loss = 0.348443\n",
      "epoch 2705  Loss = 0.348422\n",
      "epoch 2706  Loss = 0.348401\n",
      "epoch 2707  Loss = 0.348380\n",
      "epoch 2708  Loss = 0.348359\n",
      "epoch 2709  Loss = 0.348338\n",
      "epoch 2710  Loss = 0.348318\n",
      "epoch 2711  Loss = 0.348297\n",
      "epoch 2712  Loss = 0.348276\n",
      "epoch 2713  Loss = 0.348255\n",
      "epoch 2714  Loss = 0.348235\n",
      "epoch 2715  Loss = 0.348214\n",
      "epoch 2716  Loss = 0.348193\n",
      "epoch 2717  Loss = 0.348172\n",
      "epoch 2718  Loss = 0.348152\n",
      "epoch 2719  Loss = 0.348131\n",
      "epoch 2720  Loss = 0.348110\n",
      "epoch 2721  Loss = 0.348090\n",
      "epoch 2722  Loss = 0.348069\n",
      "epoch 2723  Loss = 0.348048\n",
      "epoch 2724  Loss = 0.348028\n",
      "epoch 2725  Loss = 0.348007\n",
      "epoch 2726  Loss = 0.347987\n",
      "epoch 2727  Loss = 0.347966\n",
      "epoch 2728  Loss = 0.347946\n",
      "epoch 2729  Loss = 0.347925\n",
      "epoch 2730  Loss = 0.347905\n",
      "epoch 2731  Loss = 0.347884\n",
      "epoch 2732  Loss = 0.347863\n",
      "epoch 2733  Loss = 0.347843\n",
      "epoch 2734  Loss = 0.347823\n",
      "epoch 2735  Loss = 0.347802\n",
      "epoch 2736  Loss = 0.347782\n",
      "epoch 2737  Loss = 0.347761\n",
      "epoch 2738  Loss = 0.347741\n",
      "epoch 2739  Loss = 0.347720\n",
      "epoch 2740  Loss = 0.347700\n",
      "epoch 2741  Loss = 0.347680\n",
      "epoch 2742  Loss = 0.347659\n",
      "epoch 2743  Loss = 0.347639\n",
      "epoch 2744  Loss = 0.347619\n",
      "epoch 2745  Loss = 0.347598\n",
      "epoch 2746  Loss = 0.347578\n",
      "epoch 2747  Loss = 0.347558\n",
      "epoch 2748  Loss = 0.347537\n",
      "epoch 2749  Loss = 0.347517\n",
      "epoch 2750  Loss = 0.347497\n",
      "epoch 2751  Loss = 0.347477\n",
      "epoch 2752  Loss = 0.347456\n",
      "epoch 2753  Loss = 0.347436\n",
      "epoch 2754  Loss = 0.347416\n",
      "epoch 2755  Loss = 0.347396\n",
      "epoch 2756  Loss = 0.347375\n",
      "epoch 2757  Loss = 0.347355\n",
      "epoch 2758  Loss = 0.347335\n",
      "epoch 2759  Loss = 0.347315\n",
      "epoch 2760  Loss = 0.347295\n",
      "epoch 2761  Loss = 0.347275\n",
      "epoch 2762  Loss = 0.347255\n",
      "epoch 2763  Loss = 0.347235\n",
      "epoch 2764  Loss = 0.347214\n",
      "epoch 2765  Loss = 0.347194\n",
      "epoch 2766  Loss = 0.347174\n",
      "epoch 2767  Loss = 0.347154\n",
      "epoch 2768  Loss = 0.347134\n",
      "epoch 2769  Loss = 0.347114\n",
      "epoch 2770  Loss = 0.347094\n",
      "epoch 2771  Loss = 0.347074\n",
      "epoch 2772  Loss = 0.347054\n",
      "epoch 2773  Loss = 0.347034\n",
      "epoch 2774  Loss = 0.347014\n",
      "epoch 2775  Loss = 0.346994\n",
      "epoch 2776  Loss = 0.346975\n",
      "epoch 2777  Loss = 0.346955\n",
      "epoch 2778  Loss = 0.346935\n",
      "epoch 2779  Loss = 0.346915\n",
      "epoch 2780  Loss = 0.346895\n",
      "epoch 2781  Loss = 0.346875\n",
      "epoch 2782  Loss = 0.346855\n",
      "epoch 2783  Loss = 0.346835\n",
      "epoch 2784  Loss = 0.346816\n",
      "epoch 2785  Loss = 0.346796\n",
      "epoch 2786  Loss = 0.346776\n",
      "epoch 2787  Loss = 0.346756\n",
      "epoch 2788  Loss = 0.346736\n",
      "epoch 2789  Loss = 0.346717\n",
      "epoch 2790  Loss = 0.346697\n",
      "epoch 2791  Loss = 0.346677\n",
      "epoch 2792  Loss = 0.346657\n",
      "epoch 2793  Loss = 0.346638\n",
      "epoch 2794  Loss = 0.346618\n",
      "epoch 2795  Loss = 0.346598\n",
      "epoch 2796  Loss = 0.346579\n",
      "epoch 2797  Loss = 0.346559\n",
      "epoch 2798  Loss = 0.346539\n",
      "epoch 2799  Loss = 0.346520\n",
      "epoch 2800  Loss = 0.346500\n",
      "epoch 2801  Loss = 0.346480\n",
      "epoch 2802  Loss = 0.346461\n",
      "epoch 2803  Loss = 0.346441\n",
      "epoch 2804  Loss = 0.346422\n",
      "epoch 2805  Loss = 0.346402\n",
      "epoch 2806  Loss = 0.346383\n",
      "epoch 2807  Loss = 0.346363\n",
      "epoch 2808  Loss = 0.346343\n",
      "epoch 2809  Loss = 0.346324\n",
      "epoch 2810  Loss = 0.346304\n",
      "epoch 2811  Loss = 0.346285\n",
      "epoch 2812  Loss = 0.346265\n",
      "epoch 2813  Loss = 0.346246\n",
      "epoch 2814  Loss = 0.346227\n",
      "epoch 2815  Loss = 0.346207\n",
      "epoch 2816  Loss = 0.346188\n",
      "epoch 2817  Loss = 0.346168\n",
      "epoch 2818  Loss = 0.346149\n",
      "epoch 2819  Loss = 0.346129\n",
      "epoch 2820  Loss = 0.346110\n",
      "epoch 2821  Loss = 0.346091\n",
      "epoch 2822  Loss = 0.346071\n",
      "epoch 2823  Loss = 0.346052\n",
      "epoch 2824  Loss = 0.346033\n",
      "epoch 2825  Loss = 0.346013\n",
      "epoch 2826  Loss = 0.345994\n",
      "epoch 2827  Loss = 0.345975\n",
      "epoch 2828  Loss = 0.345955\n",
      "epoch 2829  Loss = 0.345936\n",
      "epoch 2830  Loss = 0.345917\n",
      "epoch 2831  Loss = 0.345898\n",
      "epoch 2832  Loss = 0.345878\n",
      "epoch 2833  Loss = 0.345859\n",
      "epoch 2834  Loss = 0.345840\n",
      "epoch 2835  Loss = 0.345821\n",
      "epoch 2836  Loss = 0.345801\n",
      "epoch 2837  Loss = 0.345782\n",
      "epoch 2838  Loss = 0.345763\n",
      "epoch 2839  Loss = 0.345744\n",
      "epoch 2840  Loss = 0.345725\n",
      "epoch 2841  Loss = 0.345706\n",
      "epoch 2842  Loss = 0.345686\n",
      "epoch 2843  Loss = 0.345667\n",
      "epoch 2844  Loss = 0.345648\n",
      "epoch 2845  Loss = 0.345629\n",
      "epoch 2846  Loss = 0.345610\n",
      "epoch 2847  Loss = 0.345591\n",
      "epoch 2848  Loss = 0.345572\n",
      "epoch 2849  Loss = 0.345553\n",
      "epoch 2850  Loss = 0.345534\n",
      "epoch 2851  Loss = 0.345515\n",
      "epoch 2852  Loss = 0.345496\n",
      "epoch 2853  Loss = 0.345477\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2854  Loss = 0.345458\n",
      "epoch 2855  Loss = 0.345439\n",
      "epoch 2856  Loss = 0.345420\n",
      "epoch 2857  Loss = 0.345401\n",
      "epoch 2858  Loss = 0.345382\n",
      "epoch 2859  Loss = 0.345363\n",
      "epoch 2860  Loss = 0.345344\n",
      "epoch 2861  Loss = 0.345325\n",
      "epoch 2862  Loss = 0.345306\n",
      "epoch 2863  Loss = 0.345287\n",
      "epoch 2864  Loss = 0.345268\n",
      "epoch 2865  Loss = 0.345250\n",
      "epoch 2866  Loss = 0.345231\n",
      "epoch 2867  Loss = 0.345212\n",
      "epoch 2868  Loss = 0.345193\n",
      "epoch 2869  Loss = 0.345174\n",
      "epoch 2870  Loss = 0.345155\n",
      "epoch 2871  Loss = 0.345136\n",
      "epoch 2872  Loss = 0.345118\n",
      "epoch 2873  Loss = 0.345099\n",
      "epoch 2874  Loss = 0.345080\n",
      "epoch 2875  Loss = 0.345061\n",
      "epoch 2876  Loss = 0.345043\n",
      "epoch 2877  Loss = 0.345024\n",
      "epoch 2878  Loss = 0.345005\n",
      "epoch 2879  Loss = 0.344986\n",
      "epoch 2880  Loss = 0.344968\n",
      "epoch 2881  Loss = 0.344949\n",
      "epoch 2882  Loss = 0.344930\n",
      "epoch 2883  Loss = 0.344912\n",
      "epoch 2884  Loss = 0.344893\n",
      "epoch 2885  Loss = 0.344874\n",
      "epoch 2886  Loss = 0.344856\n",
      "epoch 2887  Loss = 0.344837\n",
      "epoch 2888  Loss = 0.344818\n",
      "epoch 2889  Loss = 0.344800\n",
      "epoch 2890  Loss = 0.344781\n",
      "epoch 2891  Loss = 0.344762\n",
      "epoch 2892  Loss = 0.344744\n",
      "epoch 2893  Loss = 0.344725\n",
      "epoch 2894  Loss = 0.344707\n",
      "epoch 2895  Loss = 0.344688\n",
      "epoch 2896  Loss = 0.344670\n",
      "epoch 2897  Loss = 0.344651\n",
      "epoch 2898  Loss = 0.344633\n",
      "epoch 2899  Loss = 0.344614\n",
      "epoch 2900  Loss = 0.344596\n",
      "epoch 2901  Loss = 0.344577\n",
      "epoch 2902  Loss = 0.344559\n",
      "epoch 2903  Loss = 0.344540\n",
      "epoch 2904  Loss = 0.344522\n",
      "epoch 2905  Loss = 0.344503\n",
      "epoch 2906  Loss = 0.344485\n",
      "epoch 2907  Loss = 0.344466\n",
      "epoch 2908  Loss = 0.344448\n",
      "epoch 2909  Loss = 0.344429\n",
      "epoch 2910  Loss = 0.344411\n",
      "epoch 2911  Loss = 0.344393\n",
      "epoch 2912  Loss = 0.344374\n",
      "epoch 2913  Loss = 0.344356\n",
      "epoch 2914  Loss = 0.344338\n",
      "epoch 2915  Loss = 0.344319\n",
      "epoch 2916  Loss = 0.344301\n",
      "epoch 2917  Loss = 0.344283\n",
      "epoch 2918  Loss = 0.344264\n",
      "epoch 2919  Loss = 0.344246\n",
      "epoch 2920  Loss = 0.344228\n",
      "epoch 2921  Loss = 0.344209\n",
      "epoch 2922  Loss = 0.344191\n",
      "epoch 2923  Loss = 0.344173\n",
      "epoch 2924  Loss = 0.344155\n",
      "epoch 2925  Loss = 0.344136\n",
      "epoch 2926  Loss = 0.344118\n",
      "epoch 2927  Loss = 0.344100\n",
      "epoch 2928  Loss = 0.344082\n",
      "epoch 2929  Loss = 0.344063\n",
      "epoch 2930  Loss = 0.344045\n",
      "epoch 2931  Loss = 0.344027\n",
      "epoch 2932  Loss = 0.344009\n",
      "epoch 2933  Loss = 0.343991\n",
      "epoch 2934  Loss = 0.343972\n",
      "epoch 2935  Loss = 0.343954\n",
      "epoch 2936  Loss = 0.343936\n",
      "epoch 2937  Loss = 0.343918\n",
      "epoch 2938  Loss = 0.343900\n",
      "epoch 2939  Loss = 0.343882\n",
      "epoch 2940  Loss = 0.343864\n",
      "epoch 2941  Loss = 0.343846\n",
      "epoch 2942  Loss = 0.343827\n",
      "epoch 2943  Loss = 0.343809\n",
      "epoch 2944  Loss = 0.343791\n",
      "epoch 2945  Loss = 0.343773\n",
      "epoch 2946  Loss = 0.343755\n",
      "epoch 2947  Loss = 0.343737\n",
      "epoch 2948  Loss = 0.343719\n",
      "epoch 2949  Loss = 0.343701\n",
      "epoch 2950  Loss = 0.343683\n",
      "epoch 2951  Loss = 0.343665\n",
      "epoch 2952  Loss = 0.343647\n",
      "epoch 2953  Loss = 0.343629\n",
      "epoch 2954  Loss = 0.343611\n",
      "epoch 2955  Loss = 0.343593\n",
      "epoch 2956  Loss = 0.343575\n",
      "epoch 2957  Loss = 0.343557\n",
      "epoch 2958  Loss = 0.343539\n",
      "epoch 2959  Loss = 0.343521\n",
      "epoch 2960  Loss = 0.343504\n",
      "epoch 2961  Loss = 0.343486\n",
      "epoch 2962  Loss = 0.343468\n",
      "epoch 2963  Loss = 0.343450\n",
      "epoch 2964  Loss = 0.343432\n",
      "epoch 2965  Loss = 0.343414\n",
      "epoch 2966  Loss = 0.343396\n",
      "epoch 2967  Loss = 0.343378\n",
      "epoch 2968  Loss = 0.343361\n",
      "epoch 2969  Loss = 0.343343\n",
      "epoch 2970  Loss = 0.343325\n",
      "epoch 2971  Loss = 0.343307\n",
      "epoch 2972  Loss = 0.343289\n",
      "epoch 2973  Loss = 0.343272\n",
      "epoch 2974  Loss = 0.343254\n",
      "epoch 2975  Loss = 0.343236\n",
      "epoch 2976  Loss = 0.343218\n",
      "epoch 2977  Loss = 0.343201\n",
      "epoch 2978  Loss = 0.343183\n",
      "epoch 2979  Loss = 0.343165\n",
      "epoch 2980  Loss = 0.343147\n",
      "epoch 2981  Loss = 0.343130\n",
      "epoch 2982  Loss = 0.343112\n",
      "epoch 2983  Loss = 0.343094\n",
      "epoch 2984  Loss = 0.343076\n",
      "epoch 2985  Loss = 0.343059\n",
      "epoch 2986  Loss = 0.343041\n",
      "epoch 2987  Loss = 0.343023\n",
      "epoch 2988  Loss = 0.343006\n",
      "epoch 2989  Loss = 0.342988\n",
      "epoch 2990  Loss = 0.342970\n",
      "epoch 2991  Loss = 0.342953\n",
      "epoch 2992  Loss = 0.342935\n",
      "epoch 2993  Loss = 0.342918\n",
      "epoch 2994  Loss = 0.342900\n",
      "epoch 2995  Loss = 0.342882\n",
      "epoch 2996  Loss = 0.342865\n",
      "epoch 2997  Loss = 0.342847\n",
      "epoch 2998  Loss = 0.342830\n",
      "epoch 2999  Loss = 0.342812\n",
      "epoch 3000  Loss = 0.342795\n",
      "epoch 3001  Loss = 0.342777\n",
      "epoch 3002  Loss = 0.342760\n",
      "epoch 3003  Loss = 0.342742\n",
      "epoch 3004  Loss = 0.342725\n",
      "epoch 3005  Loss = 0.342707\n",
      "epoch 3006  Loss = 0.342690\n",
      "epoch 3007  Loss = 0.342672\n",
      "epoch 3008  Loss = 0.342655\n",
      "epoch 3009  Loss = 0.342637\n",
      "epoch 3010  Loss = 0.342620\n",
      "epoch 3011  Loss = 0.342602\n",
      "epoch 3012  Loss = 0.342585\n",
      "epoch 3013  Loss = 0.342567\n",
      "epoch 3014  Loss = 0.342550\n",
      "epoch 3015  Loss = 0.342533\n",
      "epoch 3016  Loss = 0.342515\n",
      "epoch 3017  Loss = 0.342498\n",
      "epoch 3018  Loss = 0.342480\n",
      "epoch 3019  Loss = 0.342463\n",
      "epoch 3020  Loss = 0.342446\n",
      "epoch 3021  Loss = 0.342428\n",
      "epoch 3022  Loss = 0.342411\n",
      "epoch 3023  Loss = 0.342394\n",
      "epoch 3024  Loss = 0.342376\n",
      "epoch 3025  Loss = 0.342359\n",
      "epoch 3026  Loss = 0.342342\n",
      "epoch 3027  Loss = 0.342324\n",
      "epoch 3028  Loss = 0.342307\n",
      "epoch 3029  Loss = 0.342290\n",
      "epoch 3030  Loss = 0.342272\n",
      "epoch 3031  Loss = 0.342255\n",
      "epoch 3032  Loss = 0.342238\n",
      "epoch 3033  Loss = 0.342221\n",
      "epoch 3034  Loss = 0.342203\n",
      "epoch 3035  Loss = 0.342186\n",
      "epoch 3036  Loss = 0.342169\n",
      "epoch 3037  Loss = 0.342152\n",
      "epoch 3038  Loss = 0.342134\n",
      "epoch 3039  Loss = 0.342117\n",
      "epoch 3040  Loss = 0.342100\n",
      "epoch 3041  Loss = 0.342083\n",
      "epoch 3042  Loss = 0.342066\n",
      "epoch 3043  Loss = 0.342049\n",
      "epoch 3044  Loss = 0.342031\n",
      "epoch 3045  Loss = 0.342014\n",
      "epoch 3046  Loss = 0.341997\n",
      "epoch 3047  Loss = 0.341980\n",
      "epoch 3048  Loss = 0.341963\n",
      "epoch 3049  Loss = 0.341946\n",
      "epoch 3050  Loss = 0.341929\n",
      "epoch 3051  Loss = 0.341911\n",
      "epoch 3052  Loss = 0.341894\n",
      "epoch 3053  Loss = 0.341877\n",
      "epoch 3054  Loss = 0.341860\n",
      "epoch 3055  Loss = 0.341843\n",
      "epoch 3056  Loss = 0.341826\n",
      "epoch 3057  Loss = 0.341809\n",
      "epoch 3058  Loss = 0.341792\n",
      "epoch 3059  Loss = 0.341775\n",
      "epoch 3060  Loss = 0.341758\n",
      "epoch 3061  Loss = 0.341741\n",
      "epoch 3062  Loss = 0.341724\n",
      "epoch 3063  Loss = 0.341707\n",
      "epoch 3064  Loss = 0.341690\n",
      "epoch 3065  Loss = 0.341673\n",
      "epoch 3066  Loss = 0.341656\n",
      "epoch 3067  Loss = 0.341639\n",
      "epoch 3068  Loss = 0.341622\n",
      "epoch 3069  Loss = 0.341605\n",
      "epoch 3070  Loss = 0.341588\n",
      "epoch 3071  Loss = 0.341571\n",
      "epoch 3072  Loss = 0.341554\n",
      "epoch 3073  Loss = 0.341537\n",
      "epoch 3074  Loss = 0.341520\n",
      "epoch 3075  Loss = 0.341504\n",
      "epoch 3076  Loss = 0.341487\n",
      "epoch 3077  Loss = 0.341470\n",
      "epoch 3078  Loss = 0.341453\n",
      "epoch 3079  Loss = 0.341436\n",
      "epoch 3080  Loss = 0.341419\n",
      "epoch 3081  Loss = 0.341402\n",
      "epoch 3082  Loss = 0.341386\n",
      "epoch 3083  Loss = 0.341369\n",
      "epoch 3084  Loss = 0.341352\n",
      "epoch 3085  Loss = 0.341335\n",
      "epoch 3086  Loss = 0.341318\n",
      "epoch 3087  Loss = 0.341301\n",
      "epoch 3088  Loss = 0.341285\n",
      "epoch 3089  Loss = 0.341268\n",
      "epoch 3090  Loss = 0.341251\n",
      "epoch 3091  Loss = 0.341234\n",
      "epoch 3092  Loss = 0.341218\n",
      "epoch 3093  Loss = 0.341201\n",
      "epoch 3094  Loss = 0.341184\n",
      "epoch 3095  Loss = 0.341167\n",
      "epoch 3096  Loss = 0.341151\n",
      "epoch 3097  Loss = 0.341134\n",
      "epoch 3098  Loss = 0.341117\n",
      "epoch 3099  Loss = 0.341100\n",
      "epoch 3100  Loss = 0.341084\n",
      "epoch 3101  Loss = 0.341067\n",
      "epoch 3102  Loss = 0.341050\n",
      "epoch 3103  Loss = 0.341034\n",
      "epoch 3104  Loss = 0.341017\n",
      "epoch 3105  Loss = 0.341000\n",
      "epoch 3106  Loss = 0.340984\n",
      "epoch 3107  Loss = 0.340967\n",
      "epoch 3108  Loss = 0.340950\n",
      "epoch 3109  Loss = 0.340934\n",
      "epoch 3110  Loss = 0.340917\n",
      "epoch 3111  Loss = 0.340901\n",
      "epoch 3112  Loss = 0.340884\n",
      "epoch 3113  Loss = 0.340867\n",
      "epoch 3114  Loss = 0.340851\n",
      "epoch 3115  Loss = 0.340834\n",
      "epoch 3116  Loss = 0.340818\n",
      "epoch 3117  Loss = 0.340801\n",
      "epoch 3118  Loss = 0.340785\n",
      "epoch 3119  Loss = 0.340768\n",
      "epoch 3120  Loss = 0.340751\n",
      "epoch 3121  Loss = 0.340735\n",
      "epoch 3122  Loss = 0.340718\n",
      "epoch 3123  Loss = 0.340702\n",
      "epoch 3124  Loss = 0.340685\n",
      "epoch 3125  Loss = 0.340669\n",
      "epoch 3126  Loss = 0.340652\n",
      "epoch 3127  Loss = 0.340636\n",
      "epoch 3128  Loss = 0.340619\n",
      "epoch 3129  Loss = 0.340603\n",
      "epoch 3130  Loss = 0.340587\n",
      "epoch 3131  Loss = 0.340570\n",
      "epoch 3132  Loss = 0.340554\n",
      "epoch 3133  Loss = 0.340537\n",
      "epoch 3134  Loss = 0.340521\n",
      "epoch 3135  Loss = 0.340504\n",
      "epoch 3136  Loss = 0.340488\n",
      "epoch 3137  Loss = 0.340472\n",
      "epoch 3138  Loss = 0.340455\n",
      "epoch 3139  Loss = 0.340439\n",
      "epoch 3140  Loss = 0.340422\n",
      "epoch 3141  Loss = 0.340406\n",
      "epoch 3142  Loss = 0.340390\n",
      "epoch 3143  Loss = 0.340373\n",
      "epoch 3144  Loss = 0.340357\n",
      "epoch 3145  Loss = 0.340341\n",
      "epoch 3146  Loss = 0.340324\n",
      "epoch 3147  Loss = 0.340308\n",
      "epoch 3148  Loss = 0.340292\n",
      "epoch 3149  Loss = 0.340275\n",
      "epoch 3150  Loss = 0.340259\n",
      "epoch 3151  Loss = 0.340243\n",
      "epoch 3152  Loss = 0.340226\n",
      "epoch 3153  Loss = 0.340210\n",
      "epoch 3154  Loss = 0.340194\n",
      "epoch 3155  Loss = 0.340178\n",
      "epoch 3156  Loss = 0.340161\n",
      "epoch 3157  Loss = 0.340145\n",
      "epoch 3158  Loss = 0.340129\n",
      "epoch 3159  Loss = 0.340113\n",
      "epoch 3160  Loss = 0.340096\n",
      "epoch 3161  Loss = 0.340080\n",
      "epoch 3162  Loss = 0.340064\n",
      "epoch 3163  Loss = 0.340048\n",
      "epoch 3164  Loss = 0.340032\n",
      "epoch 3165  Loss = 0.340015\n",
      "epoch 3166  Loss = 0.339999\n",
      "epoch 3167  Loss = 0.339983\n",
      "epoch 3168  Loss = 0.339967\n",
      "epoch 3169  Loss = 0.339951\n",
      "epoch 3170  Loss = 0.339935\n",
      "epoch 3171  Loss = 0.339918\n",
      "epoch 3172  Loss = 0.339902\n",
      "epoch 3173  Loss = 0.339886\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 3174  Loss = 0.339870\n",
      "epoch 3175  Loss = 0.339854\n",
      "epoch 3176  Loss = 0.339838\n",
      "epoch 3177  Loss = 0.339822\n",
      "epoch 3178  Loss = 0.339806\n",
      "epoch 3179  Loss = 0.339790\n",
      "epoch 3180  Loss = 0.339773\n",
      "epoch 3181  Loss = 0.339757\n",
      "epoch 3182  Loss = 0.339741\n",
      "epoch 3183  Loss = 0.339725\n",
      "epoch 3184  Loss = 0.339709\n",
      "epoch 3185  Loss = 0.339693\n",
      "epoch 3186  Loss = 0.339677\n",
      "epoch 3187  Loss = 0.339661\n",
      "epoch 3188  Loss = 0.339645\n",
      "epoch 3189  Loss = 0.339629\n",
      "epoch 3190  Loss = 0.339613\n",
      "epoch 3191  Loss = 0.339597\n",
      "epoch 3192  Loss = 0.339581\n",
      "epoch 3193  Loss = 0.339565\n",
      "epoch 3194  Loss = 0.339549\n",
      "epoch 3195  Loss = 0.339533\n",
      "epoch 3196  Loss = 0.339517\n",
      "epoch 3197  Loss = 0.339501\n",
      "epoch 3198  Loss = 0.339485\n",
      "epoch 3199  Loss = 0.339469\n",
      "epoch 3200  Loss = 0.339453\n",
      "epoch 3201  Loss = 0.339438\n",
      "epoch 3202  Loss = 0.339422\n",
      "epoch 3203  Loss = 0.339406\n",
      "epoch 3204  Loss = 0.339390\n",
      "epoch 3205  Loss = 0.339374\n",
      "epoch 3206  Loss = 0.339358\n",
      "epoch 3207  Loss = 0.339342\n",
      "epoch 3208  Loss = 0.339326\n",
      "epoch 3209  Loss = 0.339310\n",
      "epoch 3210  Loss = 0.339295\n",
      "epoch 3211  Loss = 0.339279\n",
      "epoch 3212  Loss = 0.339263\n",
      "epoch 3213  Loss = 0.339247\n",
      "epoch 3214  Loss = 0.339231\n",
      "epoch 3215  Loss = 0.339215\n",
      "epoch 3216  Loss = 0.339200\n",
      "epoch 3217  Loss = 0.339184\n",
      "epoch 3218  Loss = 0.339168\n",
      "epoch 3219  Loss = 0.339152\n",
      "epoch 3220  Loss = 0.339136\n",
      "epoch 3221  Loss = 0.339121\n",
      "epoch 3222  Loss = 0.339105\n",
      "epoch 3223  Loss = 0.339089\n",
      "epoch 3224  Loss = 0.339073\n",
      "epoch 3225  Loss = 0.339058\n",
      "epoch 3226  Loss = 0.339042\n",
      "epoch 3227  Loss = 0.339026\n",
      "epoch 3228  Loss = 0.339010\n",
      "epoch 3229  Loss = 0.338995\n",
      "epoch 3230  Loss = 0.338979\n",
      "epoch 3231  Loss = 0.338963\n",
      "epoch 3232  Loss = 0.338948\n",
      "epoch 3233  Loss = 0.338932\n",
      "epoch 3234  Loss = 0.338916\n",
      "epoch 3235  Loss = 0.338901\n",
      "epoch 3236  Loss = 0.338885\n",
      "epoch 3237  Loss = 0.338869\n",
      "epoch 3238  Loss = 0.338854\n",
      "epoch 3239  Loss = 0.338838\n",
      "epoch 3240  Loss = 0.338822\n",
      "epoch 3241  Loss = 0.338807\n",
      "epoch 3242  Loss = 0.338791\n",
      "epoch 3243  Loss = 0.338775\n",
      "epoch 3244  Loss = 0.338760\n",
      "epoch 3245  Loss = 0.338744\n",
      "epoch 3246  Loss = 0.338729\n",
      "epoch 3247  Loss = 0.338713\n",
      "epoch 3248  Loss = 0.338697\n",
      "epoch 3249  Loss = 0.338682\n",
      "epoch 3250  Loss = 0.338666\n",
      "epoch 3251  Loss = 0.338651\n",
      "epoch 3252  Loss = 0.338635\n",
      "epoch 3253  Loss = 0.338620\n",
      "epoch 3254  Loss = 0.338604\n",
      "epoch 3255  Loss = 0.338589\n",
      "epoch 3256  Loss = 0.338573\n",
      "epoch 3257  Loss = 0.338557\n",
      "epoch 3258  Loss = 0.338542\n",
      "epoch 3259  Loss = 0.338526\n",
      "epoch 3260  Loss = 0.338511\n",
      "epoch 3261  Loss = 0.338495\n",
      "epoch 3262  Loss = 0.338480\n",
      "epoch 3263  Loss = 0.338464\n",
      "epoch 3264  Loss = 0.338449\n",
      "epoch 3265  Loss = 0.338434\n",
      "epoch 3266  Loss = 0.338418\n",
      "epoch 3267  Loss = 0.338403\n",
      "epoch 3268  Loss = 0.338387\n",
      "epoch 3269  Loss = 0.338372\n",
      "epoch 3270  Loss = 0.338356\n",
      "epoch 3271  Loss = 0.338341\n",
      "epoch 3272  Loss = 0.338325\n",
      "epoch 3273  Loss = 0.338310\n",
      "epoch 3274  Loss = 0.338295\n",
      "epoch 3275  Loss = 0.338279\n",
      "epoch 3276  Loss = 0.338264\n",
      "epoch 3277  Loss = 0.338248\n",
      "epoch 3278  Loss = 0.338233\n",
      "epoch 3279  Loss = 0.338218\n",
      "epoch 3280  Loss = 0.338202\n",
      "epoch 3281  Loss = 0.338187\n",
      "epoch 3282  Loss = 0.338172\n",
      "epoch 3283  Loss = 0.338156\n",
      "epoch 3284  Loss = 0.338141\n",
      "epoch 3285  Loss = 0.338126\n",
      "epoch 3286  Loss = 0.338110\n",
      "epoch 3287  Loss = 0.338095\n",
      "epoch 3288  Loss = 0.338080\n",
      "epoch 3289  Loss = 0.338064\n",
      "epoch 3290  Loss = 0.338049\n",
      "epoch 3291  Loss = 0.338034\n",
      "epoch 3292  Loss = 0.338018\n",
      "epoch 3293  Loss = 0.338003\n",
      "epoch 3294  Loss = 0.337988\n",
      "epoch 3295  Loss = 0.337973\n",
      "epoch 3296  Loss = 0.337957\n",
      "epoch 3297  Loss = 0.337942\n",
      "epoch 3298  Loss = 0.337927\n",
      "epoch 3299  Loss = 0.337912\n",
      "epoch 3300  Loss = 0.337896\n",
      "epoch 3301  Loss = 0.337881\n",
      "epoch 3302  Loss = 0.337866\n",
      "epoch 3303  Loss = 0.337851\n",
      "epoch 3304  Loss = 0.337835\n",
      "epoch 3305  Loss = 0.337820\n",
      "epoch 3306  Loss = 0.337805\n",
      "epoch 3307  Loss = 0.337790\n",
      "epoch 3308  Loss = 0.337775\n",
      "epoch 3309  Loss = 0.337760\n",
      "epoch 3310  Loss = 0.337744\n",
      "epoch 3311  Loss = 0.337729\n",
      "epoch 3312  Loss = 0.337714\n",
      "epoch 3313  Loss = 0.337699\n",
      "epoch 3314  Loss = 0.337684\n",
      "epoch 3315  Loss = 0.337669\n",
      "epoch 3316  Loss = 0.337653\n",
      "epoch 3317  Loss = 0.337638\n",
      "epoch 3318  Loss = 0.337623\n",
      "epoch 3319  Loss = 0.337608\n",
      "epoch 3320  Loss = 0.337593\n",
      "epoch 3321  Loss = 0.337578\n",
      "epoch 3322  Loss = 0.337563\n",
      "epoch 3323  Loss = 0.337548\n",
      "epoch 3324  Loss = 0.337533\n",
      "epoch 3325  Loss = 0.337518\n",
      "epoch 3326  Loss = 0.337503\n",
      "epoch 3327  Loss = 0.337487\n",
      "epoch 3328  Loss = 0.337472\n",
      "epoch 3329  Loss = 0.337457\n",
      "epoch 3330  Loss = 0.337442\n",
      "epoch 3331  Loss = 0.337427\n",
      "epoch 3332  Loss = 0.337412\n",
      "epoch 3333  Loss = 0.337397\n",
      "epoch 3334  Loss = 0.337382\n",
      "epoch 3335  Loss = 0.337367\n",
      "epoch 3336  Loss = 0.337352\n",
      "epoch 3337  Loss = 0.337337\n",
      "epoch 3338  Loss = 0.337322\n",
      "epoch 3339  Loss = 0.337307\n",
      "epoch 3340  Loss = 0.337292\n",
      "epoch 3341  Loss = 0.337277\n",
      "epoch 3342  Loss = 0.337262\n",
      "epoch 3343  Loss = 0.337247\n",
      "epoch 3344  Loss = 0.337232\n",
      "epoch 3345  Loss = 0.337217\n",
      "epoch 3346  Loss = 0.337203\n",
      "epoch 3347  Loss = 0.337188\n",
      "epoch 3348  Loss = 0.337173\n",
      "epoch 3349  Loss = 0.337158\n",
      "epoch 3350  Loss = 0.337143\n",
      "epoch 3351  Loss = 0.337128\n",
      "epoch 3352  Loss = 0.337113\n",
      "epoch 3353  Loss = 0.337098\n",
      "epoch 3354  Loss = 0.337083\n",
      "epoch 3355  Loss = 0.337068\n",
      "epoch 3356  Loss = 0.337053\n",
      "epoch 3357  Loss = 0.337039\n",
      "epoch 3358  Loss = 0.337024\n",
      "epoch 3359  Loss = 0.337009\n",
      "epoch 3360  Loss = 0.336994\n",
      "epoch 3361  Loss = 0.336979\n",
      "epoch 3362  Loss = 0.336964\n",
      "epoch 3363  Loss = 0.336949\n",
      "epoch 3364  Loss = 0.336935\n",
      "epoch 3365  Loss = 0.336920\n",
      "epoch 3366  Loss = 0.336905\n",
      "epoch 3367  Loss = 0.336890\n",
      "epoch 3368  Loss = 0.336875\n",
      "epoch 3369  Loss = 0.336861\n",
      "epoch 3370  Loss = 0.336846\n",
      "epoch 3371  Loss = 0.336831\n",
      "epoch 3372  Loss = 0.336816\n",
      "epoch 3373  Loss = 0.336801\n",
      "epoch 3374  Loss = 0.336787\n",
      "epoch 3375  Loss = 0.336772\n",
      "epoch 3376  Loss = 0.336757\n",
      "epoch 3377  Loss = 0.336742\n",
      "epoch 3378  Loss = 0.336728\n",
      "epoch 3379  Loss = 0.336713\n",
      "epoch 3380  Loss = 0.336698\n",
      "epoch 3381  Loss = 0.336683\n",
      "epoch 3382  Loss = 0.336669\n",
      "epoch 3383  Loss = 0.336654\n",
      "epoch 3384  Loss = 0.336639\n",
      "epoch 3385  Loss = 0.336625\n",
      "epoch 3386  Loss = 0.336610\n",
      "epoch 3387  Loss = 0.336595\n",
      "epoch 3388  Loss = 0.336581\n",
      "epoch 3389  Loss = 0.336566\n",
      "epoch 3390  Loss = 0.336551\n",
      "epoch 3391  Loss = 0.336537\n",
      "epoch 3392  Loss = 0.336522\n",
      "epoch 3393  Loss = 0.336507\n",
      "epoch 3394  Loss = 0.336493\n",
      "epoch 3395  Loss = 0.336478\n",
      "epoch 3396  Loss = 0.336463\n",
      "epoch 3397  Loss = 0.336449\n",
      "epoch 3398  Loss = 0.336434\n",
      "epoch 3399  Loss = 0.336419\n",
      "epoch 3400  Loss = 0.336405\n",
      "epoch 3401  Loss = 0.336390\n",
      "epoch 3402  Loss = 0.336376\n",
      "epoch 3403  Loss = 0.336361\n",
      "epoch 3404  Loss = 0.336346\n",
      "epoch 3405  Loss = 0.336332\n",
      "epoch 3406  Loss = 0.336317\n",
      "epoch 3407  Loss = 0.336303\n",
      "epoch 3408  Loss = 0.336288\n",
      "epoch 3409  Loss = 0.336274\n",
      "epoch 3410  Loss = 0.336259\n",
      "epoch 3411  Loss = 0.336244\n",
      "epoch 3412  Loss = 0.336230\n",
      "epoch 3413  Loss = 0.336215\n",
      "epoch 3414  Loss = 0.336201\n",
      "epoch 3415  Loss = 0.336186\n",
      "epoch 3416  Loss = 0.336172\n",
      "epoch 3417  Loss = 0.336157\n",
      "epoch 3418  Loss = 0.336143\n",
      "epoch 3419  Loss = 0.336128\n",
      "epoch 3420  Loss = 0.336114\n",
      "epoch 3421  Loss = 0.336099\n",
      "epoch 3422  Loss = 0.336085\n",
      "epoch 3423  Loss = 0.336070\n",
      "epoch 3424  Loss = 0.336056\n",
      "epoch 3425  Loss = 0.336041\n",
      "epoch 3426  Loss = 0.336027\n",
      "epoch 3427  Loss = 0.336012\n",
      "epoch 3428  Loss = 0.335998\n",
      "epoch 3429  Loss = 0.335984\n",
      "epoch 3430  Loss = 0.335969\n",
      "epoch 3431  Loss = 0.335955\n",
      "epoch 3432  Loss = 0.335940\n",
      "epoch 3433  Loss = 0.335926\n",
      "epoch 3434  Loss = 0.335911\n",
      "epoch 3435  Loss = 0.335897\n",
      "epoch 3436  Loss = 0.335883\n",
      "epoch 3437  Loss = 0.335868\n",
      "epoch 3438  Loss = 0.335854\n",
      "epoch 3439  Loss = 0.335839\n",
      "epoch 3440  Loss = 0.335825\n",
      "epoch 3441  Loss = 0.335811\n",
      "epoch 3442  Loss = 0.335796\n",
      "epoch 3443  Loss = 0.335782\n",
      "epoch 3444  Loss = 0.335768\n",
      "epoch 3445  Loss = 0.335753\n",
      "epoch 3446  Loss = 0.335739\n",
      "epoch 3447  Loss = 0.335724\n",
      "epoch 3448  Loss = 0.335710\n",
      "epoch 3449  Loss = 0.335696\n",
      "epoch 3450  Loss = 0.335681\n",
      "epoch 3451  Loss = 0.335667\n",
      "epoch 3452  Loss = 0.335653\n",
      "epoch 3453  Loss = 0.335639\n",
      "epoch 3454  Loss = 0.335624\n",
      "epoch 3455  Loss = 0.335610\n",
      "epoch 3456  Loss = 0.335596\n",
      "epoch 3457  Loss = 0.335581\n",
      "epoch 3458  Loss = 0.335567\n",
      "epoch 3459  Loss = 0.335553\n",
      "epoch 3460  Loss = 0.335539\n",
      "epoch 3461  Loss = 0.335524\n",
      "epoch 3462  Loss = 0.335510\n",
      "epoch 3463  Loss = 0.335496\n",
      "epoch 3464  Loss = 0.335481\n",
      "epoch 3465  Loss = 0.335467\n",
      "epoch 3466  Loss = 0.335453\n",
      "epoch 3467  Loss = 0.335439\n",
      "epoch 3468  Loss = 0.335425\n",
      "epoch 3469  Loss = 0.335410\n",
      "epoch 3470  Loss = 0.335396\n",
      "epoch 3471  Loss = 0.335382\n",
      "epoch 3472  Loss = 0.335368\n",
      "epoch 3473  Loss = 0.335354\n",
      "epoch 3474  Loss = 0.335339\n",
      "epoch 3475  Loss = 0.335325\n",
      "epoch 3476  Loss = 0.335311\n",
      "epoch 3477  Loss = 0.335297\n",
      "epoch 3478  Loss = 0.335283\n",
      "epoch 3479  Loss = 0.335268\n",
      "epoch 3480  Loss = 0.335254\n",
      "epoch 3481  Loss = 0.335240\n",
      "epoch 3482  Loss = 0.335226\n",
      "epoch 3483  Loss = 0.335212\n",
      "epoch 3484  Loss = 0.335198\n",
      "epoch 3485  Loss = 0.335184\n",
      "epoch 3486  Loss = 0.335169\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 3487  Loss = 0.335155\n",
      "epoch 3488  Loss = 0.335141\n",
      "epoch 3489  Loss = 0.335127\n",
      "epoch 3490  Loss = 0.335113\n",
      "epoch 3491  Loss = 0.335099\n",
      "epoch 3492  Loss = 0.335085\n",
      "epoch 3493  Loss = 0.335071\n",
      "epoch 3494  Loss = 0.335057\n",
      "epoch 3495  Loss = 0.335043\n",
      "epoch 3496  Loss = 0.335029\n",
      "epoch 3497  Loss = 0.335014\n",
      "epoch 3498  Loss = 0.335000\n",
      "epoch 3499  Loss = 0.334986\n",
      "epoch 3500  Loss = 0.334972\n",
      "epoch 3501  Loss = 0.334958\n",
      "epoch 3502  Loss = 0.334944\n",
      "epoch 3503  Loss = 0.334930\n",
      "epoch 3504  Loss = 0.334916\n",
      "epoch 3505  Loss = 0.334902\n",
      "epoch 3506  Loss = 0.334888\n",
      "epoch 3507  Loss = 0.334874\n",
      "epoch 3508  Loss = 0.334860\n",
      "epoch 3509  Loss = 0.334846\n",
      "epoch 3510  Loss = 0.334832\n",
      "epoch 3511  Loss = 0.334818\n",
      "epoch 3512  Loss = 0.334804\n",
      "epoch 3513  Loss = 0.334790\n",
      "epoch 3514  Loss = 0.334776\n",
      "epoch 3515  Loss = 0.334762\n",
      "epoch 3516  Loss = 0.334748\n",
      "epoch 3517  Loss = 0.334734\n",
      "epoch 3518  Loss = 0.334720\n",
      "epoch 3519  Loss = 0.334706\n",
      "epoch 3520  Loss = 0.334692\n",
      "epoch 3521  Loss = 0.334678\n",
      "epoch 3522  Loss = 0.334665\n",
      "epoch 3523  Loss = 0.334651\n",
      "epoch 3524  Loss = 0.334637\n",
      "epoch 3525  Loss = 0.334623\n",
      "epoch 3526  Loss = 0.334609\n",
      "epoch 3527  Loss = 0.334595\n",
      "epoch 3528  Loss = 0.334581\n",
      "epoch 3529  Loss = 0.334567\n",
      "epoch 3530  Loss = 0.334553\n",
      "epoch 3531  Loss = 0.334539\n",
      "epoch 3532  Loss = 0.334525\n",
      "epoch 3533  Loss = 0.334512\n",
      "epoch 3534  Loss = 0.334498\n",
      "epoch 3535  Loss = 0.334484\n",
      "epoch 3536  Loss = 0.334470\n",
      "epoch 3537  Loss = 0.334456\n",
      "epoch 3538  Loss = 0.334442\n",
      "epoch 3539  Loss = 0.334428\n",
      "epoch 3540  Loss = 0.334415\n",
      "epoch 3541  Loss = 0.334401\n",
      "epoch 3542  Loss = 0.334387\n",
      "epoch 3543  Loss = 0.334373\n",
      "epoch 3544  Loss = 0.334359\n",
      "epoch 3545  Loss = 0.334346\n",
      "epoch 3546  Loss = 0.334332\n",
      "epoch 3547  Loss = 0.334318\n",
      "epoch 3548  Loss = 0.334304\n",
      "epoch 3549  Loss = 0.334290\n",
      "epoch 3550  Loss = 0.334277\n",
      "epoch 3551  Loss = 0.334263\n",
      "epoch 3552  Loss = 0.334249\n",
      "epoch 3553  Loss = 0.334235\n",
      "epoch 3554  Loss = 0.334221\n",
      "epoch 3555  Loss = 0.334208\n",
      "epoch 3556  Loss = 0.334194\n",
      "epoch 3557  Loss = 0.334180\n",
      "epoch 3558  Loss = 0.334167\n",
      "epoch 3559  Loss = 0.334153\n",
      "epoch 3560  Loss = 0.334139\n",
      "epoch 3561  Loss = 0.334125\n",
      "epoch 3562  Loss = 0.334112\n",
      "epoch 3563  Loss = 0.334098\n",
      "epoch 3564  Loss = 0.334084\n",
      "epoch 3565  Loss = 0.334070\n",
      "epoch 3566  Loss = 0.334057\n",
      "epoch 3567  Loss = 0.334043\n",
      "epoch 3568  Loss = 0.334029\n",
      "epoch 3569  Loss = 0.334016\n",
      "epoch 3570  Loss = 0.334002\n",
      "epoch 3571  Loss = 0.333988\n",
      "epoch 3572  Loss = 0.333975\n",
      "epoch 3573  Loss = 0.333961\n",
      "epoch 3574  Loss = 0.333947\n",
      "epoch 3575  Loss = 0.333934\n",
      "epoch 3576  Loss = 0.333920\n",
      "epoch 3577  Loss = 0.333906\n",
      "epoch 3578  Loss = 0.333893\n",
      "epoch 3579  Loss = 0.333879\n",
      "epoch 3580  Loss = 0.333866\n",
      "epoch 3581  Loss = 0.333852\n",
      "epoch 3582  Loss = 0.333838\n",
      "epoch 3583  Loss = 0.333825\n",
      "epoch 3584  Loss = 0.333811\n",
      "epoch 3585  Loss = 0.333797\n",
      "epoch 3586  Loss = 0.333784\n",
      "epoch 3587  Loss = 0.333770\n",
      "epoch 3588  Loss = 0.333757\n",
      "epoch 3589  Loss = 0.333743\n",
      "epoch 3590  Loss = 0.333730\n",
      "epoch 3591  Loss = 0.333716\n",
      "epoch 3592  Loss = 0.333702\n",
      "epoch 3593  Loss = 0.333689\n",
      "epoch 3594  Loss = 0.333675\n",
      "epoch 3595  Loss = 0.333662\n",
      "epoch 3596  Loss = 0.333648\n",
      "epoch 3597  Loss = 0.333635\n",
      "epoch 3598  Loss = 0.333621\n",
      "epoch 3599  Loss = 0.333608\n",
      "epoch 3600  Loss = 0.333594\n",
      "epoch 3601  Loss = 0.333581\n",
      "epoch 3602  Loss = 0.333567\n",
      "epoch 3603  Loss = 0.333554\n",
      "epoch 3604  Loss = 0.333540\n",
      "epoch 3605  Loss = 0.333527\n",
      "epoch 3606  Loss = 0.333513\n",
      "epoch 3607  Loss = 0.333500\n",
      "epoch 3608  Loss = 0.333486\n",
      "epoch 3609  Loss = 0.333473\n",
      "epoch 3610  Loss = 0.333459\n",
      "epoch 3611  Loss = 0.333446\n",
      "epoch 3612  Loss = 0.333432\n",
      "epoch 3613  Loss = 0.333419\n",
      "epoch 3614  Loss = 0.333405\n",
      "epoch 3615  Loss = 0.333392\n",
      "epoch 3616  Loss = 0.333378\n",
      "epoch 3617  Loss = 0.333365\n",
      "epoch 3618  Loss = 0.333351\n",
      "epoch 3619  Loss = 0.333338\n",
      "epoch 3620  Loss = 0.333325\n",
      "epoch 3621  Loss = 0.333311\n",
      "epoch 3622  Loss = 0.333298\n",
      "epoch 3623  Loss = 0.333284\n",
      "epoch 3624  Loss = 0.333271\n",
      "epoch 3625  Loss = 0.333258\n",
      "epoch 3626  Loss = 0.333244\n",
      "epoch 3627  Loss = 0.333231\n",
      "epoch 3628  Loss = 0.333217\n",
      "epoch 3629  Loss = 0.333204\n",
      "epoch 3630  Loss = 0.333191\n",
      "epoch 3631  Loss = 0.333177\n",
      "epoch 3632  Loss = 0.333164\n",
      "epoch 3633  Loss = 0.333151\n",
      "epoch 3634  Loss = 0.333137\n",
      "epoch 3635  Loss = 0.333124\n",
      "epoch 3636  Loss = 0.333111\n",
      "epoch 3637  Loss = 0.333097\n",
      "epoch 3638  Loss = 0.333084\n",
      "epoch 3639  Loss = 0.333071\n",
      "epoch 3640  Loss = 0.333057\n",
      "epoch 3641  Loss = 0.333044\n",
      "epoch 3642  Loss = 0.333031\n",
      "epoch 3643  Loss = 0.333017\n",
      "epoch 3644  Loss = 0.333004\n",
      "epoch 3645  Loss = 0.332991\n",
      "epoch 3646  Loss = 0.332978\n",
      "epoch 3647  Loss = 0.332964\n",
      "epoch 3648  Loss = 0.332951\n",
      "epoch 3649  Loss = 0.332938\n",
      "epoch 3650  Loss = 0.332924\n",
      "epoch 3651  Loss = 0.332911\n",
      "epoch 3652  Loss = 0.332898\n",
      "epoch 3653  Loss = 0.332885\n",
      "epoch 3654  Loss = 0.332871\n",
      "epoch 3655  Loss = 0.332858\n",
      "epoch 3656  Loss = 0.332845\n",
      "epoch 3657  Loss = 0.332832\n",
      "epoch 3658  Loss = 0.332818\n",
      "epoch 3659  Loss = 0.332805\n",
      "epoch 3660  Loss = 0.332792\n",
      "epoch 3661  Loss = 0.332779\n",
      "epoch 3662  Loss = 0.332766\n",
      "epoch 3663  Loss = 0.332752\n",
      "epoch 3664  Loss = 0.332739\n",
      "epoch 3665  Loss = 0.332726\n",
      "epoch 3666  Loss = 0.332713\n",
      "epoch 3667  Loss = 0.332700\n",
      "epoch 3668  Loss = 0.332686\n",
      "epoch 3669  Loss = 0.332673\n",
      "epoch 3670  Loss = 0.332660\n",
      "epoch 3671  Loss = 0.332647\n",
      "epoch 3672  Loss = 0.332634\n",
      "epoch 3673  Loss = 0.332621\n",
      "epoch 3674  Loss = 0.332607\n",
      "epoch 3675  Loss = 0.332594\n",
      "epoch 3676  Loss = 0.332581\n",
      "epoch 3677  Loss = 0.332568\n",
      "epoch 3678  Loss = 0.332555\n",
      "epoch 3679  Loss = 0.332542\n",
      "epoch 3680  Loss = 0.332529\n",
      "epoch 3681  Loss = 0.332516\n",
      "epoch 3682  Loss = 0.332502\n",
      "epoch 3683  Loss = 0.332489\n",
      "epoch 3684  Loss = 0.332476\n",
      "epoch 3685  Loss = 0.332463\n",
      "epoch 3686  Loss = 0.332450\n",
      "epoch 3687  Loss = 0.332437\n",
      "epoch 3688  Loss = 0.332424\n",
      "epoch 3689  Loss = 0.332411\n",
      "epoch 3690  Loss = 0.332398\n",
      "epoch 3691  Loss = 0.332385\n",
      "epoch 3692  Loss = 0.332372\n",
      "epoch 3693  Loss = 0.332359\n",
      "epoch 3694  Loss = 0.332346\n",
      "epoch 3695  Loss = 0.332332\n",
      "epoch 3696  Loss = 0.332319\n",
      "epoch 3697  Loss = 0.332306\n",
      "epoch 3698  Loss = 0.332293\n",
      "epoch 3699  Loss = 0.332280\n",
      "epoch 3700  Loss = 0.332267\n",
      "epoch 3701  Loss = 0.332254\n",
      "epoch 3702  Loss = 0.332241\n",
      "epoch 3703  Loss = 0.332228\n",
      "epoch 3704  Loss = 0.332215\n",
      "epoch 3705  Loss = 0.332202\n",
      "epoch 3706  Loss = 0.332189\n",
      "epoch 3707  Loss = 0.332176\n",
      "epoch 3708  Loss = 0.332163\n",
      "epoch 3709  Loss = 0.332150\n",
      "epoch 3710  Loss = 0.332137\n",
      "epoch 3711  Loss = 0.332125\n",
      "epoch 3712  Loss = 0.332112\n",
      "epoch 3713  Loss = 0.332099\n",
      "epoch 3714  Loss = 0.332086\n",
      "epoch 3715  Loss = 0.332073\n",
      "epoch 3716  Loss = 0.332060\n",
      "epoch 3717  Loss = 0.332047\n",
      "epoch 3718  Loss = 0.332034\n",
      "epoch 3719  Loss = 0.332021\n",
      "epoch 3720  Loss = 0.332008\n",
      "epoch 3721  Loss = 0.331995\n",
      "epoch 3722  Loss = 0.331982\n",
      "epoch 3723  Loss = 0.331969\n",
      "epoch 3724  Loss = 0.331956\n",
      "epoch 3725  Loss = 0.331944\n",
      "epoch 3726  Loss = 0.331931\n",
      "epoch 3727  Loss = 0.331918\n",
      "epoch 3728  Loss = 0.331905\n",
      "epoch 3729  Loss = 0.331892\n",
      "epoch 3730  Loss = 0.331879\n",
      "epoch 3731  Loss = 0.331866\n",
      "epoch 3732  Loss = 0.331853\n",
      "epoch 3733  Loss = 0.331841\n",
      "epoch 3734  Loss = 0.331828\n",
      "epoch 3735  Loss = 0.331815\n",
      "epoch 3736  Loss = 0.331802\n",
      "epoch 3737  Loss = 0.331789\n",
      "epoch 3738  Loss = 0.331776\n",
      "epoch 3739  Loss = 0.331764\n",
      "epoch 3740  Loss = 0.331751\n",
      "epoch 3741  Loss = 0.331738\n",
      "epoch 3742  Loss = 0.331725\n",
      "epoch 3743  Loss = 0.331712\n",
      "epoch 3744  Loss = 0.331700\n",
      "epoch 3745  Loss = 0.331687\n",
      "epoch 3746  Loss = 0.331674\n",
      "epoch 3747  Loss = 0.331661\n",
      "epoch 3748  Loss = 0.331648\n",
      "epoch 3749  Loss = 0.331636\n",
      "epoch 3750  Loss = 0.331623\n",
      "epoch 3751  Loss = 0.331610\n",
      "epoch 3752  Loss = 0.331597\n",
      "epoch 3753  Loss = 0.331585\n",
      "epoch 3754  Loss = 0.331572\n",
      "epoch 3755  Loss = 0.331559\n",
      "epoch 3756  Loss = 0.331546\n",
      "epoch 3757  Loss = 0.331534\n",
      "epoch 3758  Loss = 0.331521\n",
      "epoch 3759  Loss = 0.331508\n",
      "epoch 3760  Loss = 0.331495\n",
      "epoch 3761  Loss = 0.331483\n",
      "epoch 3762  Loss = 0.331470\n",
      "epoch 3763  Loss = 0.331457\n",
      "epoch 3764  Loss = 0.331445\n",
      "epoch 3765  Loss = 0.331432\n",
      "epoch 3766  Loss = 0.331419\n",
      "epoch 3767  Loss = 0.331407\n",
      "epoch 3768  Loss = 0.331394\n",
      "epoch 3769  Loss = 0.331381\n",
      "epoch 3770  Loss = 0.331369\n",
      "epoch 3771  Loss = 0.331356\n",
      "epoch 3772  Loss = 0.331343\n",
      "epoch 3773  Loss = 0.331331\n",
      "epoch 3774  Loss = 0.331318\n",
      "epoch 3775  Loss = 0.331305\n",
      "epoch 3776  Loss = 0.331293\n",
      "epoch 3777  Loss = 0.331280\n",
      "epoch 3778  Loss = 0.331267\n",
      "epoch 3779  Loss = 0.331255\n",
      "epoch 3780  Loss = 0.331242\n",
      "epoch 3781  Loss = 0.331230\n",
      "epoch 3782  Loss = 0.331217\n",
      "epoch 3783  Loss = 0.331204\n",
      "epoch 3784  Loss = 0.331192\n",
      "epoch 3785  Loss = 0.331179\n",
      "epoch 3786  Loss = 0.331167\n",
      "epoch 3787  Loss = 0.331154\n",
      "epoch 3788  Loss = 0.331141\n",
      "epoch 3789  Loss = 0.331129\n",
      "epoch 3790  Loss = 0.331116\n",
      "epoch 3791  Loss = 0.331104\n",
      "epoch 3792  Loss = 0.331091\n",
      "epoch 3793  Loss = 0.331079\n",
      "epoch 3794  Loss = 0.331066\n",
      "epoch 3795  Loss = 0.331054\n",
      "epoch 3796  Loss = 0.331041\n",
      "epoch 3797  Loss = 0.331028\n",
      "epoch 3798  Loss = 0.331016\n",
      "epoch 3799  Loss = 0.331003\n",
      "epoch 3800  Loss = 0.330991\n",
      "epoch 3801  Loss = 0.330978\n",
      "epoch 3802  Loss = 0.330966\n",
      "epoch 3803  Loss = 0.330953\n",
      "epoch 3804  Loss = 0.330941\n",
      "epoch 3805  Loss = 0.330928\n",
      "epoch 3806  Loss = 0.330916\n",
      "epoch 3807  Loss = 0.330903\n",
      "epoch 3808  Loss = 0.330891\n",
      "epoch 3809  Loss = 0.330878\n",
      "epoch 3810  Loss = 0.330866\n",
      "epoch 3811  Loss = 0.330853\n",
      "epoch 3812  Loss = 0.330841\n",
      "epoch 3813  Loss = 0.330829\n",
      "epoch 3814  Loss = 0.330816\n",
      "epoch 3815  Loss = 0.330804\n",
      "epoch 3816  Loss = 0.330791\n",
      "epoch 3817  Loss = 0.330779\n",
      "epoch 3818  Loss = 0.330766\n",
      "epoch 3819  Loss = 0.330754\n",
      "epoch 3820  Loss = 0.330742\n",
      "epoch 3821  Loss = 0.330729\n",
      "epoch 3822  Loss = 0.330717\n",
      "epoch 3823  Loss = 0.330704\n",
      "epoch 3824  Loss = 0.330692\n",
      "epoch 3825  Loss = 0.330680\n",
      "epoch 3826  Loss = 0.330667\n",
      "epoch 3827  Loss = 0.330655\n",
      "epoch 3828  Loss = 0.330642\n",
      "epoch 3829  Loss = 0.330630\n",
      "epoch 3830  Loss = 0.330618\n",
      "epoch 3831  Loss = 0.330605\n",
      "epoch 3832  Loss = 0.330593\n",
      "epoch 3833  Loss = 0.330581\n",
      "epoch 3834  Loss = 0.330568\n",
      "epoch 3835  Loss = 0.330556\n",
      "epoch 3836  Loss = 0.330543\n",
      "epoch 3837  Loss = 0.330531\n",
      "epoch 3838  Loss = 0.330519\n",
      "epoch 3839  Loss = 0.330507\n",
      "epoch 3840  Loss = 0.330494\n",
      "epoch 3841  Loss = 0.330482\n",
      "epoch 3842  Loss = 0.330470\n",
      "epoch 3843  Loss = 0.330457\n",
      "epoch 3844  Loss = 0.330445\n",
      "epoch 3845  Loss = 0.330433\n",
      "epoch 3846  Loss = 0.330420\n",
      "epoch 3847  Loss = 0.330408\n",
      "epoch 3848  Loss = 0.330396\n",
      "epoch 3849  Loss = 0.330384\n",
      "epoch 3850  Loss = 0.330371\n",
      "epoch 3851  Loss = 0.330359\n",
      "epoch 3852  Loss = 0.330347\n",
      "epoch 3853  Loss = 0.330334\n",
      "epoch 3854  Loss = 0.330322\n",
      "epoch 3855  Loss = 0.330310\n",
      "epoch 3856  Loss = 0.330298\n",
      "epoch 3857  Loss = 0.330285\n",
      "epoch 3858  Loss = 0.330273\n",
      "epoch 3859  Loss = 0.330261\n",
      "epoch 3860  Loss = 0.330249\n",
      "epoch 3861  Loss = 0.330236\n",
      "epoch 3862  Loss = 0.330224\n",
      "epoch 3863  Loss = 0.330212\n",
      "epoch 3864  Loss = 0.330200\n",
      "epoch 3865  Loss = 0.330188\n",
      "epoch 3866  Loss = 0.330175\n",
      "epoch 3867  Loss = 0.330163\n",
      "epoch 3868  Loss = 0.330151\n",
      "epoch 3869  Loss = 0.330139\n",
      "epoch 3870  Loss = 0.330127\n",
      "epoch 3871  Loss = 0.330115\n",
      "epoch 3872  Loss = 0.330102\n",
      "epoch 3873  Loss = 0.330090\n",
      "epoch 3874  Loss = 0.330078\n",
      "epoch 3875  Loss = 0.330066\n",
      "epoch 3876  Loss = 0.330054\n",
      "epoch 3877  Loss = 0.330042\n",
      "epoch 3878  Loss = 0.330030\n",
      "epoch 3879  Loss = 0.330017\n",
      "epoch 3880  Loss = 0.330005\n",
      "epoch 3881  Loss = 0.329993\n",
      "epoch 3882  Loss = 0.329981\n",
      "epoch 3883  Loss = 0.329969\n",
      "epoch 3884  Loss = 0.329957\n",
      "epoch 3885  Loss = 0.329945\n",
      "epoch 3886  Loss = 0.329933\n",
      "epoch 3887  Loss = 0.329920\n",
      "epoch 3888  Loss = 0.329908\n",
      "epoch 3889  Loss = 0.329896\n",
      "epoch 3890  Loss = 0.329884\n",
      "epoch 3891  Loss = 0.329872\n",
      "epoch 3892  Loss = 0.329860\n",
      "epoch 3893  Loss = 0.329848\n",
      "epoch 3894  Loss = 0.329836\n",
      "epoch 3895  Loss = 0.329824\n",
      "epoch 3896  Loss = 0.329812\n",
      "epoch 3897  Loss = 0.329800\n",
      "epoch 3898  Loss = 0.329788\n",
      "epoch 3899  Loss = 0.329776\n",
      "epoch 3900  Loss = 0.329764\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 3901  Loss = 0.329752\n",
      "epoch 3902  Loss = 0.329740\n",
      "epoch 3903  Loss = 0.329728\n",
      "epoch 3904  Loss = 0.329716\n",
      "epoch 3905  Loss = 0.329704\n",
      "epoch 3906  Loss = 0.329692\n",
      "epoch 3907  Loss = 0.329680\n",
      "epoch 3908  Loss = 0.329668\n",
      "epoch 3909  Loss = 0.329656\n",
      "epoch 3910  Loss = 0.329644\n",
      "epoch 3911  Loss = 0.329632\n",
      "epoch 3912  Loss = 0.329620\n",
      "epoch 3913  Loss = 0.329608\n",
      "epoch 3914  Loss = 0.329596\n",
      "epoch 3915  Loss = 0.329584\n",
      "epoch 3916  Loss = 0.329572\n",
      "epoch 3917  Loss = 0.329560\n",
      "epoch 3918  Loss = 0.329548\n",
      "epoch 3919  Loss = 0.329536\n",
      "epoch 3920  Loss = 0.329524\n",
      "epoch 3921  Loss = 0.329512\n",
      "epoch 3922  Loss = 0.329500\n",
      "epoch 3923  Loss = 0.329488\n",
      "epoch 3924  Loss = 0.329476\n",
      "epoch 3925  Loss = 0.329464\n",
      "epoch 3926  Loss = 0.329453\n",
      "epoch 3927  Loss = 0.329441\n",
      "epoch 3928  Loss = 0.329429\n",
      "epoch 3929  Loss = 0.329417\n",
      "epoch 3930  Loss = 0.329405\n",
      "epoch 3931  Loss = 0.329393\n",
      "epoch 3932  Loss = 0.329381\n",
      "epoch 3933  Loss = 0.329369\n",
      "epoch 3934  Loss = 0.329357\n",
      "epoch 3935  Loss = 0.329346\n",
      "epoch 3936  Loss = 0.329334\n",
      "epoch 3937  Loss = 0.329322\n",
      "epoch 3938  Loss = 0.329310\n",
      "epoch 3939  Loss = 0.329298\n",
      "epoch 3940  Loss = 0.329286\n",
      "epoch 3941  Loss = 0.329274\n",
      "epoch 3942  Loss = 0.329263\n",
      "epoch 3943  Loss = 0.329251\n",
      "epoch 3944  Loss = 0.329239\n",
      "epoch 3945  Loss = 0.329227\n",
      "epoch 3946  Loss = 0.329215\n",
      "epoch 3947  Loss = 0.329204\n",
      "epoch 3948  Loss = 0.329192\n",
      "epoch 3949  Loss = 0.329180\n",
      "epoch 3950  Loss = 0.329168\n",
      "epoch 3951  Loss = 0.329156\n",
      "epoch 3952  Loss = 0.329145\n",
      "epoch 3953  Loss = 0.329133\n",
      "epoch 3954  Loss = 0.329121\n",
      "epoch 3955  Loss = 0.329109\n",
      "epoch 3956  Loss = 0.329097\n",
      "epoch 3957  Loss = 0.329086\n",
      "epoch 3958  Loss = 0.329074\n",
      "epoch 3959  Loss = 0.329062\n",
      "epoch 3960  Loss = 0.329050\n",
      "epoch 3961  Loss = 0.329039\n",
      "epoch 3962  Loss = 0.329027\n",
      "epoch 3963  Loss = 0.329015\n",
      "epoch 3964  Loss = 0.329003\n",
      "epoch 3965  Loss = 0.328992\n",
      "epoch 3966  Loss = 0.328980\n",
      "epoch 3967  Loss = 0.328968\n",
      "epoch 3968  Loss = 0.328957\n",
      "epoch 3969  Loss = 0.328945\n",
      "epoch 3970  Loss = 0.328933\n",
      "epoch 3971  Loss = 0.328921\n",
      "epoch 3972  Loss = 0.328910\n",
      "epoch 3973  Loss = 0.328898\n",
      "epoch 3974  Loss = 0.328886\n",
      "epoch 3975  Loss = 0.328875\n",
      "epoch 3976  Loss = 0.328863\n",
      "epoch 3977  Loss = 0.328851\n",
      "epoch 3978  Loss = 0.328840\n",
      "epoch 3979  Loss = 0.328828\n",
      "epoch 3980  Loss = 0.328816\n",
      "epoch 3981  Loss = 0.328805\n",
      "epoch 3982  Loss = 0.328793\n",
      "epoch 3983  Loss = 0.328781\n",
      "epoch 3984  Loss = 0.328770\n",
      "epoch 3985  Loss = 0.328758\n",
      "epoch 3986  Loss = 0.328747\n",
      "epoch 3987  Loss = 0.328735\n",
      "epoch 3988  Loss = 0.328723\n",
      "epoch 3989  Loss = 0.328712\n",
      "epoch 3990  Loss = 0.328700\n",
      "epoch 3991  Loss = 0.328688\n",
      "epoch 3992  Loss = 0.328677\n",
      "epoch 3993  Loss = 0.328665\n",
      "epoch 3994  Loss = 0.328654\n",
      "epoch 3995  Loss = 0.328642\n",
      "epoch 3996  Loss = 0.328630\n",
      "epoch 3997  Loss = 0.328619\n",
      "epoch 3998  Loss = 0.328607\n",
      "epoch 3999  Loss = 0.328596\n",
      "epoch 4000  Loss = 0.328584\n",
      "epoch 4001  Loss = 0.328573\n",
      "epoch 4002  Loss = 0.328561\n",
      "epoch 4003  Loss = 0.328549\n",
      "epoch 4004  Loss = 0.328538\n",
      "epoch 4005  Loss = 0.328526\n",
      "epoch 4006  Loss = 0.328515\n",
      "epoch 4007  Loss = 0.328503\n",
      "epoch 4008  Loss = 0.328492\n",
      "epoch 4009  Loss = 0.328480\n",
      "epoch 4010  Loss = 0.328469\n",
      "epoch 4011  Loss = 0.328457\n",
      "epoch 4012  Loss = 0.328446\n",
      "epoch 4013  Loss = 0.328434\n",
      "epoch 4014  Loss = 0.328423\n",
      "epoch 4015  Loss = 0.328411\n",
      "epoch 4016  Loss = 0.328400\n",
      "epoch 4017  Loss = 0.328388\n",
      "epoch 4018  Loss = 0.328377\n",
      "epoch 4019  Loss = 0.328365\n",
      "epoch 4020  Loss = 0.328354\n",
      "epoch 4021  Loss = 0.328342\n",
      "epoch 4022  Loss = 0.328331\n",
      "epoch 4023  Loss = 0.328319\n",
      "epoch 4024  Loss = 0.328308\n",
      "epoch 4025  Loss = 0.328296\n",
      "epoch 4026  Loss = 0.328285\n",
      "epoch 4027  Loss = 0.328274\n",
      "epoch 4028  Loss = 0.328262\n",
      "epoch 4029  Loss = 0.328251\n",
      "epoch 4030  Loss = 0.328239\n",
      "epoch 4031  Loss = 0.328228\n",
      "epoch 4032  Loss = 0.328216\n",
      "epoch 4033  Loss = 0.328205\n",
      "epoch 4034  Loss = 0.328194\n",
      "epoch 4035  Loss = 0.328182\n",
      "epoch 4036  Loss = 0.328171\n",
      "epoch 4037  Loss = 0.328159\n",
      "epoch 4038  Loss = 0.328148\n",
      "epoch 4039  Loss = 0.328136\n",
      "epoch 4040  Loss = 0.328125\n",
      "epoch 4041  Loss = 0.328114\n",
      "epoch 4042  Loss = 0.328102\n",
      "epoch 4043  Loss = 0.328091\n",
      "epoch 4044  Loss = 0.328080\n",
      "epoch 4045  Loss = 0.328068\n",
      "epoch 4046  Loss = 0.328057\n",
      "epoch 4047  Loss = 0.328045\n",
      "epoch 4048  Loss = 0.328034\n",
      "epoch 4049  Loss = 0.328023\n",
      "epoch 4050  Loss = 0.328011\n",
      "epoch 4051  Loss = 0.328000\n",
      "epoch 4052  Loss = 0.327989\n",
      "epoch 4053  Loss = 0.327977\n",
      "epoch 4054  Loss = 0.327966\n",
      "epoch 4055  Loss = 0.327955\n",
      "epoch 4056  Loss = 0.327943\n",
      "epoch 4057  Loss = 0.327932\n",
      "epoch 4058  Loss = 0.327921\n",
      "epoch 4059  Loss = 0.327909\n",
      "epoch 4060  Loss = 0.327898\n",
      "epoch 4061  Loss = 0.327887\n",
      "epoch 4062  Loss = 0.327876\n",
      "epoch 4063  Loss = 0.327864\n",
      "epoch 4064  Loss = 0.327853\n",
      "epoch 4065  Loss = 0.327842\n",
      "epoch 4066  Loss = 0.327830\n",
      "epoch 4067  Loss = 0.327819\n",
      "epoch 4068  Loss = 0.327808\n",
      "epoch 4069  Loss = 0.327797\n",
      "epoch 4070  Loss = 0.327785\n",
      "epoch 4071  Loss = 0.327774\n",
      "epoch 4072  Loss = 0.327763\n",
      "epoch 4073  Loss = 0.327752\n",
      "epoch 4074  Loss = 0.327740\n",
      "epoch 4075  Loss = 0.327729\n",
      "epoch 4076  Loss = 0.327718\n",
      "epoch 4077  Loss = 0.327707\n",
      "epoch 4078  Loss = 0.327695\n",
      "epoch 4079  Loss = 0.327684\n",
      "epoch 4080  Loss = 0.327673\n",
      "epoch 4081  Loss = 0.327662\n",
      "epoch 4082  Loss = 0.327650\n",
      "epoch 4083  Loss = 0.327639\n",
      "epoch 4084  Loss = 0.327628\n",
      "epoch 4085  Loss = 0.327617\n",
      "epoch 4086  Loss = 0.327606\n",
      "epoch 4087  Loss = 0.327594\n",
      "epoch 4088  Loss = 0.327583\n",
      "epoch 4089  Loss = 0.327572\n",
      "epoch 4090  Loss = 0.327561\n",
      "epoch 4091  Loss = 0.327550\n",
      "epoch 4092  Loss = 0.327539\n",
      "epoch 4093  Loss = 0.327527\n",
      "epoch 4094  Loss = 0.327516\n",
      "epoch 4095  Loss = 0.327505\n",
      "epoch 4096  Loss = 0.327494\n",
      "epoch 4097  Loss = 0.327483\n",
      "epoch 4098  Loss = 0.327472\n",
      "epoch 4099  Loss = 0.327460\n",
      "epoch 4100  Loss = 0.327449\n",
      "epoch 4101  Loss = 0.327438\n",
      "epoch 4102  Loss = 0.327427\n",
      "epoch 4103  Loss = 0.327416\n",
      "epoch 4104  Loss = 0.327405\n",
      "epoch 4105  Loss = 0.327394\n",
      "epoch 4106  Loss = 0.327383\n",
      "epoch 4107  Loss = 0.327371\n",
      "epoch 4108  Loss = 0.327360\n",
      "epoch 4109  Loss = 0.327349\n",
      "epoch 4110  Loss = 0.327338\n",
      "epoch 4111  Loss = 0.327327\n",
      "epoch 4112  Loss = 0.327316\n",
      "epoch 4113  Loss = 0.327305\n",
      "epoch 4114  Loss = 0.327294\n",
      "epoch 4115  Loss = 0.327283\n",
      "epoch 4116  Loss = 0.327272\n",
      "epoch 4117  Loss = 0.327260\n",
      "epoch 4118  Loss = 0.327249\n",
      "epoch 4119  Loss = 0.327238\n",
      "epoch 4120  Loss = 0.327227\n",
      "epoch 4121  Loss = 0.327216\n",
      "epoch 4122  Loss = 0.327205\n",
      "epoch 4123  Loss = 0.327194\n",
      "epoch 4124  Loss = 0.327183\n",
      "epoch 4125  Loss = 0.327172\n",
      "epoch 4126  Loss = 0.327161\n",
      "epoch 4127  Loss = 0.327150\n",
      "epoch 4128  Loss = 0.327139\n",
      "epoch 4129  Loss = 0.327128\n",
      "epoch 4130  Loss = 0.327117\n",
      "epoch 4131  Loss = 0.327106\n",
      "epoch 4132  Loss = 0.327095\n",
      "epoch 4133  Loss = 0.327084\n",
      "epoch 4134  Loss = 0.327073\n",
      "epoch 4135  Loss = 0.327062\n",
      "epoch 4136  Loss = 0.327051\n",
      "epoch 4137  Loss = 0.327040\n",
      "epoch 4138  Loss = 0.327029\n",
      "epoch 4139  Loss = 0.327018\n",
      "epoch 4140  Loss = 0.327007\n",
      "epoch 4141  Loss = 0.326996\n",
      "epoch 4142  Loss = 0.326985\n",
      "epoch 4143  Loss = 0.326974\n",
      "epoch 4144  Loss = 0.326963\n",
      "epoch 4145  Loss = 0.326952\n",
      "epoch 4146  Loss = 0.326941\n",
      "epoch 4147  Loss = 0.326930\n",
      "epoch 4148  Loss = 0.326919\n",
      "epoch 4149  Loss = 0.326908\n",
      "epoch 4150  Loss = 0.326897\n",
      "epoch 4151  Loss = 0.326886\n",
      "epoch 4152  Loss = 0.326875\n",
      "epoch 4153  Loss = 0.326864\n",
      "epoch 4154  Loss = 0.326853\n",
      "epoch 4155  Loss = 0.326842\n",
      "epoch 4156  Loss = 0.326832\n",
      "epoch 4157  Loss = 0.326821\n",
      "epoch 4158  Loss = 0.326810\n",
      "epoch 4159  Loss = 0.326799\n",
      "epoch 4160  Loss = 0.326788\n",
      "epoch 4161  Loss = 0.326777\n",
      "epoch 4162  Loss = 0.326766\n",
      "epoch 4163  Loss = 0.326755\n",
      "epoch 4164  Loss = 0.326744\n",
      "epoch 4165  Loss = 0.326733\n",
      "epoch 4166  Loss = 0.326722\n",
      "epoch 4167  Loss = 0.326712\n",
      "epoch 4168  Loss = 0.326701\n",
      "epoch 4169  Loss = 0.326690\n",
      "epoch 4170  Loss = 0.326679\n",
      "epoch 4171  Loss = 0.326668\n",
      "epoch 4172  Loss = 0.326657\n",
      "epoch 4173  Loss = 0.326646\n",
      "epoch 4174  Loss = 0.326635\n",
      "epoch 4175  Loss = 0.326625\n",
      "epoch 4176  Loss = 0.326614\n",
      "epoch 4177  Loss = 0.326603\n",
      "epoch 4178  Loss = 0.326592\n",
      "epoch 4179  Loss = 0.326581\n",
      "epoch 4180  Loss = 0.326570\n",
      "epoch 4181  Loss = 0.326559\n",
      "epoch 4182  Loss = 0.326549\n",
      "epoch 4183  Loss = 0.326538\n",
      "epoch 4184  Loss = 0.326527\n",
      "epoch 4185  Loss = 0.326516\n",
      "epoch 4186  Loss = 0.326505\n",
      "epoch 4187  Loss = 0.326494\n",
      "epoch 4188  Loss = 0.326484\n",
      "epoch 4189  Loss = 0.326473\n",
      "epoch 4190  Loss = 0.326462\n",
      "epoch 4191  Loss = 0.326451\n",
      "epoch 4192  Loss = 0.326440\n",
      "epoch 4193  Loss = 0.326430\n",
      "epoch 4194  Loss = 0.326419\n",
      "epoch 4195  Loss = 0.326408\n",
      "epoch 4196  Loss = 0.326397\n",
      "epoch 4197  Loss = 0.326387\n",
      "epoch 4198  Loss = 0.326376\n",
      "epoch 4199  Loss = 0.326365\n",
      "epoch 4200  Loss = 0.326354\n",
      "epoch 4201  Loss = 0.326343\n",
      "epoch 4202  Loss = 0.326333\n",
      "epoch 4203  Loss = 0.326322\n",
      "epoch 4204  Loss = 0.326311\n",
      "epoch 4205  Loss = 0.326300\n",
      "epoch 4206  Loss = 0.326290\n",
      "epoch 4207  Loss = 0.326279\n",
      "epoch 4208  Loss = 0.326268\n",
      "epoch 4209  Loss = 0.326257\n",
      "epoch 4210  Loss = 0.326247\n",
      "epoch 4211  Loss = 0.326236\n",
      "epoch 4212  Loss = 0.326225\n",
      "epoch 4213  Loss = 0.326214\n",
      "epoch 4214  Loss = 0.326204\n",
      "epoch 4215  Loss = 0.326193\n",
      "epoch 4216  Loss = 0.326182\n",
      "epoch 4217  Loss = 0.326171\n",
      "epoch 4218  Loss = 0.326161\n",
      "epoch 4219  Loss = 0.326150\n",
      "epoch 4220  Loss = 0.326139\n",
      "epoch 4221  Loss = 0.326129\n",
      "epoch 4222  Loss = 0.326118\n",
      "epoch 4223  Loss = 0.326107\n",
      "epoch 4224  Loss = 0.326097\n",
      "epoch 4225  Loss = 0.326086\n",
      "epoch 4226  Loss = 0.326075\n",
      "epoch 4227  Loss = 0.326064\n",
      "epoch 4228  Loss = 0.326054\n",
      "epoch 4229  Loss = 0.326043\n",
      "epoch 4230  Loss = 0.326032\n",
      "epoch 4231  Loss = 0.326022\n",
      "epoch 4232  Loss = 0.326011\n",
      "epoch 4233  Loss = 0.326000\n",
      "epoch 4234  Loss = 0.325990\n",
      "epoch 4235  Loss = 0.325979\n",
      "epoch 4236  Loss = 0.325968\n",
      "epoch 4237  Loss = 0.325958\n",
      "epoch 4238  Loss = 0.325947\n",
      "epoch 4239  Loss = 0.325936\n",
      "epoch 4240  Loss = 0.325926\n",
      "epoch 4241  Loss = 0.325915\n",
      "epoch 4242  Loss = 0.325905\n",
      "epoch 4243  Loss = 0.325894\n",
      "epoch 4244  Loss = 0.325883\n",
      "epoch 4245  Loss = 0.325873\n",
      "epoch 4246  Loss = 0.325862\n",
      "epoch 4247  Loss = 0.325851\n",
      "epoch 4248  Loss = 0.325841\n",
      "epoch 4249  Loss = 0.325830\n",
      "epoch 4250  Loss = 0.325819\n",
      "epoch 4251  Loss = 0.325809\n",
      "epoch 4252  Loss = 0.325798\n",
      "epoch 4253  Loss = 0.325788\n",
      "epoch 4254  Loss = 0.325777\n",
      "epoch 4255  Loss = 0.325766\n",
      "epoch 4256  Loss = 0.325756\n",
      "epoch 4257  Loss = 0.325745\n",
      "epoch 4258  Loss = 0.325735\n",
      "epoch 4259  Loss = 0.325724\n",
      "epoch 4260  Loss = 0.325713\n",
      "epoch 4261  Loss = 0.325703\n",
      "epoch 4262  Loss = 0.325692\n",
      "epoch 4263  Loss = 0.325682\n",
      "epoch 4264  Loss = 0.325671\n",
      "epoch 4265  Loss = 0.325661\n",
      "epoch 4266  Loss = 0.325650\n",
      "epoch 4267  Loss = 0.325639\n",
      "epoch 4268  Loss = 0.325629\n",
      "epoch 4269  Loss = 0.325618\n",
      "epoch 4270  Loss = 0.325608\n",
      "epoch 4271  Loss = 0.325597\n",
      "epoch 4272  Loss = 0.325587\n",
      "epoch 4273  Loss = 0.325576\n",
      "epoch 4274  Loss = 0.325566\n",
      "epoch 4275  Loss = 0.325555\n",
      "epoch 4276  Loss = 0.325544\n",
      "epoch 4277  Loss = 0.325534\n",
      "epoch 4278  Loss = 0.325523\n",
      "epoch 4279  Loss = 0.325513\n",
      "epoch 4280  Loss = 0.325502\n",
      "epoch 4281  Loss = 0.325492\n",
      "epoch 4282  Loss = 0.325481\n",
      "epoch 4283  Loss = 0.325471\n",
      "epoch 4284  Loss = 0.325460\n",
      "epoch 4285  Loss = 0.325450\n",
      "epoch 4286  Loss = 0.325439\n",
      "epoch 4287  Loss = 0.325429\n",
      "epoch 4288  Loss = 0.325418\n",
      "epoch 4289  Loss = 0.325408\n",
      "epoch 4290  Loss = 0.325397\n",
      "epoch 4291  Loss = 0.325387\n",
      "epoch 4292  Loss = 0.325376\n",
      "epoch 4293  Loss = 0.325366\n",
      "epoch 4294  Loss = 0.325355\n",
      "epoch 4295  Loss = 0.325345\n",
      "epoch 4296  Loss = 0.325334\n",
      "epoch 4297  Loss = 0.325324\n",
      "epoch 4298  Loss = 0.325313\n",
      "epoch 4299  Loss = 0.325303\n",
      "epoch 4300  Loss = 0.325292\n",
      "epoch 4301  Loss = 0.325282\n",
      "epoch 4302  Loss = 0.325271\n",
      "epoch 4303  Loss = 0.325261\n",
      "epoch 4304  Loss = 0.325250\n",
      "epoch 4305  Loss = 0.325240\n",
      "epoch 4306  Loss = 0.325229\n",
      "epoch 4307  Loss = 0.325219\n",
      "epoch 4308  Loss = 0.325208\n",
      "epoch 4309  Loss = 0.325198\n",
      "epoch 4310  Loss = 0.325187\n",
      "epoch 4311  Loss = 0.325177\n",
      "epoch 4312  Loss = 0.325167\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 4313  Loss = 0.325156\n",
      "epoch 4314  Loss = 0.325146\n",
      "epoch 4315  Loss = 0.325135\n",
      "epoch 4316  Loss = 0.325125\n",
      "epoch 4317  Loss = 0.325114\n",
      "epoch 4318  Loss = 0.325104\n",
      "epoch 4319  Loss = 0.325093\n",
      "epoch 4320  Loss = 0.325083\n",
      "epoch 4321  Loss = 0.325073\n",
      "epoch 4322  Loss = 0.325062\n",
      "epoch 4323  Loss = 0.325052\n",
      "epoch 4324  Loss = 0.325041\n",
      "epoch 4325  Loss = 0.325031\n",
      "epoch 4326  Loss = 0.325020\n",
      "epoch 4327  Loss = 0.325010\n",
      "epoch 4328  Loss = 0.325000\n",
      "epoch 4329  Loss = 0.324989\n",
      "epoch 4330  Loss = 0.324979\n",
      "epoch 4331  Loss = 0.324968\n",
      "epoch 4332  Loss = 0.324958\n",
      "epoch 4333  Loss = 0.324948\n",
      "epoch 4334  Loss = 0.324937\n",
      "epoch 4335  Loss = 0.324927\n",
      "epoch 4336  Loss = 0.324916\n",
      "epoch 4337  Loss = 0.324906\n",
      "epoch 4338  Loss = 0.324896\n",
      "epoch 4339  Loss = 0.324885\n",
      "epoch 4340  Loss = 0.324875\n",
      "epoch 4341  Loss = 0.324864\n",
      "epoch 4342  Loss = 0.324854\n",
      "epoch 4343  Loss = 0.324844\n",
      "epoch 4344  Loss = 0.324833\n",
      "epoch 4345  Loss = 0.324823\n",
      "epoch 4346  Loss = 0.324813\n",
      "epoch 4347  Loss = 0.324802\n",
      "epoch 4348  Loss = 0.324792\n",
      "epoch 4349  Loss = 0.324781\n",
      "epoch 4350  Loss = 0.324771\n",
      "epoch 4351  Loss = 0.324761\n",
      "epoch 4352  Loss = 0.324750\n",
      "epoch 4353  Loss = 0.324740\n",
      "epoch 4354  Loss = 0.324730\n",
      "epoch 4355  Loss = 0.324719\n",
      "epoch 4356  Loss = 0.324709\n",
      "epoch 4357  Loss = 0.324699\n",
      "epoch 4358  Loss = 0.324688\n",
      "epoch 4359  Loss = 0.324678\n",
      "epoch 4360  Loss = 0.324667\n",
      "epoch 4361  Loss = 0.324657\n",
      "epoch 4362  Loss = 0.324647\n",
      "epoch 4363  Loss = 0.324636\n",
      "epoch 4364  Loss = 0.324626\n",
      "epoch 4365  Loss = 0.324616\n",
      "epoch 4366  Loss = 0.324605\n",
      "epoch 4367  Loss = 0.324595\n",
      "epoch 4368  Loss = 0.324585\n",
      "epoch 4369  Loss = 0.324574\n",
      "epoch 4370  Loss = 0.324564\n",
      "epoch 4371  Loss = 0.324554\n",
      "epoch 4372  Loss = 0.324543\n",
      "epoch 4373  Loss = 0.324533\n",
      "epoch 4374  Loss = 0.324523\n",
      "epoch 4375  Loss = 0.324512\n",
      "epoch 4376  Loss = 0.324502\n",
      "epoch 4377  Loss = 0.324492\n",
      "epoch 4378  Loss = 0.324482\n",
      "epoch 4379  Loss = 0.324471\n",
      "epoch 4380  Loss = 0.324461\n",
      "epoch 4381  Loss = 0.324451\n",
      "epoch 4382  Loss = 0.324440\n",
      "epoch 4383  Loss = 0.324430\n",
      "epoch 4384  Loss = 0.324420\n",
      "epoch 4385  Loss = 0.324409\n",
      "epoch 4386  Loss = 0.324399\n",
      "epoch 4387  Loss = 0.324389\n",
      "epoch 4388  Loss = 0.324378\n",
      "epoch 4389  Loss = 0.324368\n",
      "epoch 4390  Loss = 0.324358\n",
      "epoch 4391  Loss = 0.324348\n",
      "epoch 4392  Loss = 0.324337\n",
      "epoch 4393  Loss = 0.324327\n",
      "epoch 4394  Loss = 0.324317\n",
      "epoch 4395  Loss = 0.324306\n",
      "epoch 4396  Loss = 0.324296\n",
      "epoch 4397  Loss = 0.324286\n",
      "epoch 4398  Loss = 0.324276\n",
      "epoch 4399  Loss = 0.324265\n",
      "epoch 4400  Loss = 0.324255\n",
      "epoch 4401  Loss = 0.324245\n",
      "epoch 4402  Loss = 0.324234\n",
      "epoch 4403  Loss = 0.324224\n",
      "epoch 4404  Loss = 0.324214\n",
      "epoch 4405  Loss = 0.324204\n",
      "epoch 4406  Loss = 0.324193\n",
      "epoch 4407  Loss = 0.324183\n",
      "epoch 4408  Loss = 0.324173\n",
      "epoch 4409  Loss = 0.324163\n",
      "epoch 4410  Loss = 0.324152\n",
      "epoch 4411  Loss = 0.324142\n",
      "epoch 4412  Loss = 0.324132\n",
      "epoch 4413  Loss = 0.324122\n",
      "epoch 4414  Loss = 0.324111\n",
      "epoch 4415  Loss = 0.324101\n",
      "epoch 4416  Loss = 0.324091\n",
      "epoch 4417  Loss = 0.324080\n",
      "epoch 4418  Loss = 0.324070\n",
      "epoch 4419  Loss = 0.324060\n",
      "epoch 4420  Loss = 0.324050\n",
      "epoch 4421  Loss = 0.324039\n",
      "epoch 4422  Loss = 0.324029\n",
      "epoch 4423  Loss = 0.324019\n",
      "epoch 4424  Loss = 0.324009\n",
      "epoch 4425  Loss = 0.323998\n",
      "epoch 4426  Loss = 0.323988\n",
      "epoch 4427  Loss = 0.323978\n",
      "epoch 4428  Loss = 0.323968\n",
      "epoch 4429  Loss = 0.323957\n",
      "epoch 4430  Loss = 0.323947\n",
      "epoch 4431  Loss = 0.323937\n",
      "epoch 4432  Loss = 0.323927\n",
      "epoch 4433  Loss = 0.323917\n",
      "epoch 4434  Loss = 0.323906\n",
      "epoch 4435  Loss = 0.323896\n",
      "epoch 4436  Loss = 0.323886\n",
      "epoch 4437  Loss = 0.323876\n",
      "epoch 4438  Loss = 0.323865\n",
      "epoch 4439  Loss = 0.323855\n",
      "epoch 4440  Loss = 0.323845\n",
      "epoch 4441  Loss = 0.323835\n",
      "epoch 4442  Loss = 0.323824\n",
      "epoch 4443  Loss = 0.323814\n",
      "epoch 4444  Loss = 0.323804\n",
      "epoch 4445  Loss = 0.323794\n",
      "epoch 4446  Loss = 0.323784\n",
      "epoch 4447  Loss = 0.323773\n",
      "epoch 4448  Loss = 0.323763\n",
      "epoch 4449  Loss = 0.323753\n",
      "epoch 4450  Loss = 0.323743\n",
      "epoch 4451  Loss = 0.323733\n",
      "epoch 4452  Loss = 0.323722\n",
      "epoch 4453  Loss = 0.323712\n",
      "epoch 4454  Loss = 0.323702\n",
      "epoch 4455  Loss = 0.323692\n",
      "epoch 4456  Loss = 0.323681\n",
      "epoch 4457  Loss = 0.323671\n",
      "epoch 4458  Loss = 0.323661\n",
      "epoch 4459  Loss = 0.323651\n",
      "epoch 4460  Loss = 0.323641\n",
      "epoch 4461  Loss = 0.323630\n",
      "epoch 4462  Loss = 0.323620\n",
      "epoch 4463  Loss = 0.323610\n",
      "epoch 4464  Loss = 0.323600\n",
      "epoch 4465  Loss = 0.323590\n",
      "epoch 4466  Loss = 0.323579\n",
      "epoch 4467  Loss = 0.323569\n",
      "epoch 4468  Loss = 0.323559\n",
      "epoch 4469  Loss = 0.323549\n",
      "epoch 4470  Loss = 0.323539\n",
      "epoch 4471  Loss = 0.323528\n",
      "epoch 4472  Loss = 0.323518\n",
      "epoch 4473  Loss = 0.323508\n",
      "epoch 4474  Loss = 0.323498\n",
      "epoch 4475  Loss = 0.323488\n",
      "epoch 4476  Loss = 0.323477\n",
      "epoch 4477  Loss = 0.323467\n",
      "epoch 4478  Loss = 0.323457\n",
      "epoch 4479  Loss = 0.323447\n",
      "epoch 4480  Loss = 0.323437\n",
      "epoch 4481  Loss = 0.323427\n",
      "epoch 4482  Loss = 0.323416\n",
      "epoch 4483  Loss = 0.323406\n",
      "epoch 4484  Loss = 0.323396\n",
      "epoch 4485  Loss = 0.323386\n",
      "epoch 4486  Loss = 0.323376\n",
      "epoch 4487  Loss = 0.323365\n",
      "epoch 4488  Loss = 0.323355\n",
      "epoch 4489  Loss = 0.323345\n",
      "epoch 4490  Loss = 0.323335\n",
      "epoch 4491  Loss = 0.323325\n",
      "epoch 4492  Loss = 0.323314\n",
      "epoch 4493  Loss = 0.323304\n",
      "epoch 4494  Loss = 0.323294\n",
      "epoch 4495  Loss = 0.323284\n",
      "epoch 4496  Loss = 0.323274\n",
      "epoch 4497  Loss = 0.323264\n",
      "epoch 4498  Loss = 0.323253\n",
      "epoch 4499  Loss = 0.323243\n",
      "epoch 4500  Loss = 0.323233\n",
      "epoch 4501  Loss = 0.323223\n",
      "epoch 4502  Loss = 0.323213\n",
      "epoch 4503  Loss = 0.323203\n",
      "epoch 4504  Loss = 0.323192\n",
      "epoch 4505  Loss = 0.323182\n",
      "epoch 4506  Loss = 0.323172\n",
      "epoch 4507  Loss = 0.323162\n",
      "epoch 4508  Loss = 0.323152\n",
      "epoch 4509  Loss = 0.323141\n",
      "epoch 4510  Loss = 0.323131\n",
      "epoch 4511  Loss = 0.323121\n",
      "epoch 4512  Loss = 0.323111\n",
      "epoch 4513  Loss = 0.323101\n",
      "epoch 4514  Loss = 0.323091\n",
      "epoch 4515  Loss = 0.323080\n",
      "epoch 4516  Loss = 0.323070\n",
      "epoch 4517  Loss = 0.323060\n",
      "epoch 4518  Loss = 0.323050\n",
      "epoch 4519  Loss = 0.323040\n",
      "epoch 4520  Loss = 0.323030\n",
      "epoch 4521  Loss = 0.323019\n",
      "epoch 4522  Loss = 0.323009\n",
      "epoch 4523  Loss = 0.322999\n",
      "epoch 4524  Loss = 0.322989\n",
      "epoch 4525  Loss = 0.322979\n",
      "epoch 4526  Loss = 0.322969\n",
      "epoch 4527  Loss = 0.322958\n",
      "epoch 4528  Loss = 0.322948\n",
      "epoch 4529  Loss = 0.322938\n",
      "epoch 4530  Loss = 0.322928\n",
      "epoch 4531  Loss = 0.322918\n",
      "epoch 4532  Loss = 0.322908\n",
      "epoch 4533  Loss = 0.322897\n",
      "epoch 4534  Loss = 0.322887\n",
      "epoch 4535  Loss = 0.322877\n",
      "epoch 4536  Loss = 0.322867\n",
      "epoch 4537  Loss = 0.322857\n",
      "epoch 4538  Loss = 0.322847\n",
      "epoch 4539  Loss = 0.322836\n",
      "epoch 4540  Loss = 0.322826\n",
      "epoch 4541  Loss = 0.322816\n",
      "epoch 4542  Loss = 0.322806\n",
      "epoch 4543  Loss = 0.322796\n",
      "epoch 4544  Loss = 0.322786\n",
      "epoch 4545  Loss = 0.322775\n",
      "epoch 4546  Loss = 0.322765\n",
      "epoch 4547  Loss = 0.322755\n",
      "epoch 4548  Loss = 0.322745\n",
      "epoch 4549  Loss = 0.322735\n",
      "epoch 4550  Loss = 0.322725\n",
      "epoch 4551  Loss = 0.322714\n",
      "epoch 4552  Loss = 0.322704\n",
      "epoch 4553  Loss = 0.322694\n",
      "epoch 4554  Loss = 0.322684\n",
      "epoch 4555  Loss = 0.322674\n",
      "epoch 4556  Loss = 0.322664\n",
      "epoch 4557  Loss = 0.322653\n",
      "epoch 4558  Loss = 0.322643\n",
      "epoch 4559  Loss = 0.322633\n",
      "epoch 4560  Loss = 0.322623\n",
      "epoch 4561  Loss = 0.322613\n",
      "epoch 4562  Loss = 0.322603\n",
      "epoch 4563  Loss = 0.322592\n",
      "epoch 4564  Loss = 0.322582\n",
      "epoch 4565  Loss = 0.322572\n",
      "epoch 4566  Loss = 0.322562\n",
      "epoch 4567  Loss = 0.322552\n",
      "epoch 4568  Loss = 0.322542\n",
      "epoch 4569  Loss = 0.322532\n",
      "epoch 4570  Loss = 0.322521\n",
      "epoch 4571  Loss = 0.322511\n",
      "epoch 4572  Loss = 0.322501\n",
      "epoch 4573  Loss = 0.322491\n",
      "epoch 4574  Loss = 0.322481\n",
      "epoch 4575  Loss = 0.322471\n",
      "epoch 4576  Loss = 0.322460\n",
      "epoch 4577  Loss = 0.322450\n",
      "epoch 4578  Loss = 0.322440\n",
      "epoch 4579  Loss = 0.322430\n",
      "epoch 4580  Loss = 0.322420\n",
      "epoch 4581  Loss = 0.322409\n",
      "epoch 4582  Loss = 0.322399\n",
      "epoch 4583  Loss = 0.322389\n",
      "epoch 4584  Loss = 0.322379\n",
      "epoch 4585  Loss = 0.322369\n",
      "epoch 4586  Loss = 0.322359\n",
      "epoch 4587  Loss = 0.322349\n",
      "epoch 4588  Loss = 0.322338\n",
      "epoch 4589  Loss = 0.322328\n",
      "epoch 4590  Loss = 0.322318\n",
      "epoch 4591  Loss = 0.322308\n",
      "epoch 4592  Loss = 0.322298\n",
      "epoch 4593  Loss = 0.322287\n",
      "epoch 4594  Loss = 0.322277\n",
      "epoch 4595  Loss = 0.322267\n",
      "epoch 4596  Loss = 0.322257\n",
      "epoch 4597  Loss = 0.322247\n",
      "epoch 4598  Loss = 0.322237\n",
      "epoch 4599  Loss = 0.322226\n",
      "epoch 4600  Loss = 0.322216\n",
      "epoch 4601  Loss = 0.322206\n",
      "epoch 4602  Loss = 0.322196\n",
      "epoch 4603  Loss = 0.322186\n",
      "epoch 4604  Loss = 0.322176\n",
      "epoch 4605  Loss = 0.322165\n",
      "epoch 4606  Loss = 0.322155\n",
      "epoch 4607  Loss = 0.322145\n",
      "epoch 4608  Loss = 0.322135\n",
      "epoch 4609  Loss = 0.322125\n",
      "epoch 4610  Loss = 0.322114\n",
      "epoch 4611  Loss = 0.322104\n",
      "epoch 4612  Loss = 0.322094\n",
      "epoch 4613  Loss = 0.322084\n",
      "epoch 4614  Loss = 0.322074\n",
      "epoch 4615  Loss = 0.322064\n",
      "epoch 4616  Loss = 0.322053\n",
      "epoch 4617  Loss = 0.322043\n",
      "epoch 4618  Loss = 0.322033\n",
      "epoch 4619  Loss = 0.322023\n",
      "epoch 4620  Loss = 0.322013\n",
      "epoch 4621  Loss = 0.322002\n",
      "epoch 4622  Loss = 0.321992\n",
      "epoch 4623  Loss = 0.321982\n",
      "epoch 4624  Loss = 0.321972\n",
      "epoch 4625  Loss = 0.321962\n",
      "epoch 4626  Loss = 0.321952\n",
      "epoch 4627  Loss = 0.321941\n",
      "epoch 4628  Loss = 0.321931\n",
      "epoch 4629  Loss = 0.321921\n",
      "epoch 4630  Loss = 0.321911\n",
      "epoch 4631  Loss = 0.321901\n",
      "epoch 4632  Loss = 0.321890\n",
      "epoch 4633  Loss = 0.321880\n",
      "epoch 4634  Loss = 0.321870\n",
      "epoch 4635  Loss = 0.321860\n",
      "epoch 4636  Loss = 0.321850\n",
      "epoch 4637  Loss = 0.321839\n",
      "epoch 4638  Loss = 0.321829\n",
      "epoch 4639  Loss = 0.321819\n",
      "epoch 4640  Loss = 0.321809\n",
      "epoch 4641  Loss = 0.321799\n",
      "epoch 4642  Loss = 0.321788\n",
      "epoch 4643  Loss = 0.321778\n",
      "epoch 4644  Loss = 0.321768\n",
      "epoch 4645  Loss = 0.321758\n",
      "epoch 4646  Loss = 0.321748\n",
      "epoch 4647  Loss = 0.321737\n",
      "epoch 4648  Loss = 0.321727\n",
      "epoch 4649  Loss = 0.321717\n",
      "epoch 4650  Loss = 0.321707\n",
      "epoch 4651  Loss = 0.321697\n",
      "epoch 4652  Loss = 0.321686\n",
      "epoch 4653  Loss = 0.321676\n",
      "epoch 4654  Loss = 0.321666\n",
      "epoch 4655  Loss = 0.321656\n",
      "epoch 4656  Loss = 0.321645\n",
      "epoch 4657  Loss = 0.321635\n",
      "epoch 4658  Loss = 0.321625\n",
      "epoch 4659  Loss = 0.321615\n",
      "epoch 4660  Loss = 0.321605\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 4661  Loss = 0.321594\n",
      "epoch 4662  Loss = 0.321584\n",
      "epoch 4663  Loss = 0.321574\n",
      "epoch 4664  Loss = 0.321564\n",
      "epoch 4665  Loss = 0.321553\n",
      "epoch 4666  Loss = 0.321543\n",
      "epoch 4667  Loss = 0.321533\n",
      "epoch 4668  Loss = 0.321523\n",
      "epoch 4669  Loss = 0.321513\n",
      "epoch 4670  Loss = 0.321502\n",
      "epoch 4671  Loss = 0.321492\n",
      "epoch 4672  Loss = 0.321482\n",
      "epoch 4673  Loss = 0.321472\n",
      "epoch 4674  Loss = 0.321461\n",
      "epoch 4675  Loss = 0.321451\n",
      "epoch 4676  Loss = 0.321441\n",
      "epoch 4677  Loss = 0.321431\n",
      "epoch 4678  Loss = 0.321421\n",
      "epoch 4679  Loss = 0.321410\n",
      "epoch 4680  Loss = 0.321400\n",
      "epoch 4681  Loss = 0.321390\n",
      "epoch 4682  Loss = 0.321380\n",
      "epoch 4683  Loss = 0.321369\n",
      "epoch 4684  Loss = 0.321359\n",
      "epoch 4685  Loss = 0.321349\n",
      "epoch 4686  Loss = 0.321339\n",
      "epoch 4687  Loss = 0.321328\n",
      "epoch 4688  Loss = 0.321318\n",
      "epoch 4689  Loss = 0.321308\n",
      "epoch 4690  Loss = 0.321298\n",
      "epoch 4691  Loss = 0.321287\n",
      "epoch 4692  Loss = 0.321277\n",
      "epoch 4693  Loss = 0.321267\n",
      "epoch 4694  Loss = 0.321257\n",
      "epoch 4695  Loss = 0.321246\n",
      "epoch 4696  Loss = 0.321236\n",
      "epoch 4697  Loss = 0.321226\n",
      "epoch 4698  Loss = 0.321216\n",
      "epoch 4699  Loss = 0.321205\n",
      "epoch 4700  Loss = 0.321195\n",
      "epoch 4701  Loss = 0.321185\n",
      "epoch 4702  Loss = 0.321175\n",
      "epoch 4703  Loss = 0.321164\n",
      "epoch 4704  Loss = 0.321154\n",
      "epoch 4705  Loss = 0.321144\n",
      "epoch 4706  Loss = 0.321134\n",
      "epoch 4707  Loss = 0.321123\n",
      "epoch 4708  Loss = 0.321113\n",
      "epoch 4709  Loss = 0.321103\n",
      "epoch 4710  Loss = 0.321092\n",
      "epoch 4711  Loss = 0.321082\n",
      "epoch 4712  Loss = 0.321072\n",
      "epoch 4713  Loss = 0.321062\n",
      "epoch 4714  Loss = 0.321051\n",
      "epoch 4715  Loss = 0.321041\n",
      "epoch 4716  Loss = 0.321031\n",
      "epoch 4717  Loss = 0.321021\n",
      "epoch 4718  Loss = 0.321010\n",
      "epoch 4719  Loss = 0.321000\n",
      "epoch 4720  Loss = 0.320990\n",
      "epoch 4721  Loss = 0.320979\n",
      "epoch 4722  Loss = 0.320969\n",
      "epoch 4723  Loss = 0.320959\n",
      "epoch 4724  Loss = 0.320949\n",
      "epoch 4725  Loss = 0.320938\n",
      "epoch 4726  Loss = 0.320928\n",
      "epoch 4727  Loss = 0.320918\n",
      "epoch 4728  Loss = 0.320907\n",
      "epoch 4729  Loss = 0.320897\n",
      "epoch 4730  Loss = 0.320887\n",
      "epoch 4731  Loss = 0.320877\n",
      "epoch 4732  Loss = 0.320866\n",
      "epoch 4733  Loss = 0.320856\n",
      "epoch 4734  Loss = 0.320846\n",
      "epoch 4735  Loss = 0.320835\n",
      "epoch 4736  Loss = 0.320825\n",
      "epoch 4737  Loss = 0.320815\n",
      "epoch 4738  Loss = 0.320805\n",
      "epoch 4739  Loss = 0.320794\n",
      "epoch 4740  Loss = 0.320784\n",
      "epoch 4741  Loss = 0.320774\n",
      "epoch 4742  Loss = 0.320763\n",
      "epoch 4743  Loss = 0.320753\n",
      "epoch 4744  Loss = 0.320743\n",
      "epoch 4745  Loss = 0.320732\n",
      "epoch 4746  Loss = 0.320722\n",
      "epoch 4747  Loss = 0.320712\n",
      "epoch 4748  Loss = 0.320701\n",
      "epoch 4749  Loss = 0.320691\n",
      "epoch 4750  Loss = 0.320681\n",
      "epoch 4751  Loss = 0.320671\n",
      "epoch 4752  Loss = 0.320660\n",
      "epoch 4753  Loss = 0.320650\n",
      "epoch 4754  Loss = 0.320640\n",
      "epoch 4755  Loss = 0.320629\n",
      "epoch 4756  Loss = 0.320619\n",
      "epoch 4757  Loss = 0.320609\n",
      "epoch 4758  Loss = 0.320598\n",
      "epoch 4759  Loss = 0.320588\n",
      "epoch 4760  Loss = 0.320578\n",
      "epoch 4761  Loss = 0.320567\n",
      "epoch 4762  Loss = 0.320557\n",
      "epoch 4763  Loss = 0.320547\n",
      "epoch 4764  Loss = 0.320536\n",
      "epoch 4765  Loss = 0.320526\n",
      "epoch 4766  Loss = 0.320516\n",
      "epoch 4767  Loss = 0.320505\n",
      "epoch 4768  Loss = 0.320495\n",
      "epoch 4769  Loss = 0.320485\n",
      "epoch 4770  Loss = 0.320474\n",
      "epoch 4771  Loss = 0.320464\n",
      "epoch 4772  Loss = 0.320454\n",
      "epoch 4773  Loss = 0.320443\n",
      "epoch 4774  Loss = 0.320433\n",
      "epoch 4775  Loss = 0.320423\n",
      "epoch 4776  Loss = 0.320412\n",
      "epoch 4777  Loss = 0.320402\n",
      "epoch 4778  Loss = 0.320392\n",
      "epoch 4779  Loss = 0.320381\n",
      "epoch 4780  Loss = 0.320371\n",
      "epoch 4781  Loss = 0.320361\n",
      "epoch 4782  Loss = 0.320350\n",
      "epoch 4783  Loss = 0.320340\n",
      "epoch 4784  Loss = 0.320330\n",
      "epoch 4785  Loss = 0.320319\n",
      "epoch 4786  Loss = 0.320309\n",
      "epoch 4787  Loss = 0.320299\n",
      "epoch 4788  Loss = 0.320288\n",
      "epoch 4789  Loss = 0.320278\n",
      "epoch 4790  Loss = 0.320267\n",
      "epoch 4791  Loss = 0.320257\n",
      "epoch 4792  Loss = 0.320247\n",
      "epoch 4793  Loss = 0.320236\n",
      "epoch 4794  Loss = 0.320226\n",
      "epoch 4795  Loss = 0.320216\n",
      "epoch 4796  Loss = 0.320205\n",
      "epoch 4797  Loss = 0.320195\n",
      "epoch 4798  Loss = 0.320185\n",
      "epoch 4799  Loss = 0.320174\n",
      "epoch 4800  Loss = 0.320164\n",
      "epoch 4801  Loss = 0.320153\n",
      "epoch 4802  Loss = 0.320143\n",
      "epoch 4803  Loss = 0.320133\n",
      "epoch 4804  Loss = 0.320122\n",
      "epoch 4805  Loss = 0.320112\n",
      "epoch 4806  Loss = 0.320102\n",
      "epoch 4807  Loss = 0.320091\n",
      "epoch 4808  Loss = 0.320081\n",
      "epoch 4809  Loss = 0.320070\n",
      "epoch 4810  Loss = 0.320060\n",
      "epoch 4811  Loss = 0.320050\n",
      "epoch 4812  Loss = 0.320039\n",
      "epoch 4813  Loss = 0.320029\n",
      "epoch 4814  Loss = 0.320019\n",
      "epoch 4815  Loss = 0.320008\n",
      "epoch 4816  Loss = 0.319998\n",
      "epoch 4817  Loss = 0.319987\n",
      "epoch 4818  Loss = 0.319977\n",
      "epoch 4819  Loss = 0.319967\n",
      "epoch 4820  Loss = 0.319956\n",
      "epoch 4821  Loss = 0.319946\n",
      "epoch 4822  Loss = 0.319935\n",
      "epoch 4823  Loss = 0.319925\n",
      "epoch 4824  Loss = 0.319915\n",
      "epoch 4825  Loss = 0.319904\n",
      "epoch 4826  Loss = 0.319894\n",
      "epoch 4827  Loss = 0.319883\n",
      "epoch 4828  Loss = 0.319873\n",
      "epoch 4829  Loss = 0.319863\n",
      "epoch 4830  Loss = 0.319852\n",
      "epoch 4831  Loss = 0.319842\n",
      "epoch 4832  Loss = 0.319831\n",
      "epoch 4833  Loss = 0.319821\n",
      "epoch 4834  Loss = 0.319811\n",
      "epoch 4835  Loss = 0.319800\n",
      "epoch 4836  Loss = 0.319790\n",
      "epoch 4837  Loss = 0.319780\n",
      "epoch 4838  Loss = 0.319769\n",
      "epoch 4839  Loss = 0.319759\n",
      "epoch 4840  Loss = 0.319748\n",
      "epoch 4841  Loss = 0.319738\n",
      "epoch 4842  Loss = 0.319727\n",
      "epoch 4843  Loss = 0.319717\n",
      "epoch 4844  Loss = 0.319707\n",
      "epoch 4845  Loss = 0.319696\n",
      "epoch 4846  Loss = 0.319686\n",
      "epoch 4847  Loss = 0.319675\n",
      "epoch 4848  Loss = 0.319665\n",
      "epoch 4849  Loss = 0.319655\n",
      "epoch 4850  Loss = 0.319644\n",
      "epoch 4851  Loss = 0.319634\n",
      "epoch 4852  Loss = 0.319623\n",
      "epoch 4853  Loss = 0.319613\n",
      "epoch 4854  Loss = 0.319603\n",
      "epoch 4855  Loss = 0.319592\n",
      "epoch 4856  Loss = 0.319582\n",
      "epoch 4857  Loss = 0.319571\n",
      "epoch 4858  Loss = 0.319561\n",
      "epoch 4859  Loss = 0.319550\n",
      "epoch 4860  Loss = 0.319540\n",
      "epoch 4861  Loss = 0.319530\n",
      "epoch 4862  Loss = 0.319519\n",
      "epoch 4863  Loss = 0.319509\n",
      "epoch 4864  Loss = 0.319498\n",
      "epoch 4865  Loss = 0.319488\n",
      "epoch 4866  Loss = 0.319478\n",
      "epoch 4867  Loss = 0.319467\n",
      "epoch 4868  Loss = 0.319457\n",
      "epoch 4869  Loss = 0.319446\n",
      "epoch 4870  Loss = 0.319436\n",
      "epoch 4871  Loss = 0.319425\n",
      "epoch 4872  Loss = 0.319415\n",
      "epoch 4873  Loss = 0.319405\n",
      "epoch 4874  Loss = 0.319394\n",
      "epoch 4875  Loss = 0.319384\n",
      "epoch 4876  Loss = 0.319373\n",
      "epoch 4877  Loss = 0.319363\n",
      "epoch 4878  Loss = 0.319352\n",
      "epoch 4879  Loss = 0.319342\n",
      "epoch 4880  Loss = 0.319332\n",
      "epoch 4881  Loss = 0.319321\n",
      "epoch 4882  Loss = 0.319311\n",
      "epoch 4883  Loss = 0.319300\n",
      "epoch 4884  Loss = 0.319290\n",
      "epoch 4885  Loss = 0.319279\n",
      "epoch 4886  Loss = 0.319269\n",
      "epoch 4887  Loss = 0.319259\n",
      "epoch 4888  Loss = 0.319248\n",
      "epoch 4889  Loss = 0.319238\n",
      "epoch 4890  Loss = 0.319227\n",
      "epoch 4891  Loss = 0.319217\n",
      "epoch 4892  Loss = 0.319206\n",
      "epoch 4893  Loss = 0.319196\n",
      "epoch 4894  Loss = 0.319186\n",
      "epoch 4895  Loss = 0.319175\n",
      "epoch 4896  Loss = 0.319165\n",
      "epoch 4897  Loss = 0.319154\n",
      "epoch 4898  Loss = 0.319144\n",
      "epoch 4899  Loss = 0.319133\n",
      "epoch 4900  Loss = 0.319123\n",
      "epoch 4901  Loss = 0.319113\n",
      "epoch 4902  Loss = 0.319102\n",
      "epoch 4903  Loss = 0.319092\n",
      "epoch 4904  Loss = 0.319081\n",
      "epoch 4905  Loss = 0.319071\n",
      "epoch 4906  Loss = 0.319060\n",
      "epoch 4907  Loss = 0.319050\n",
      "epoch 4908  Loss = 0.319039\n",
      "epoch 4909  Loss = 0.319029\n",
      "epoch 4910  Loss = 0.319019\n",
      "epoch 4911  Loss = 0.319008\n",
      "epoch 4912  Loss = 0.318998\n",
      "epoch 4913  Loss = 0.318987\n",
      "epoch 4914  Loss = 0.318977\n",
      "epoch 4915  Loss = 0.318966\n",
      "epoch 4916  Loss = 0.318956\n",
      "epoch 4917  Loss = 0.318945\n",
      "epoch 4918  Loss = 0.318935\n",
      "epoch 4919  Loss = 0.318925\n",
      "epoch 4920  Loss = 0.318914\n",
      "epoch 4921  Loss = 0.318904\n",
      "epoch 4922  Loss = 0.318893\n",
      "epoch 4923  Loss = 0.318883\n",
      "epoch 4924  Loss = 0.318872\n",
      "epoch 4925  Loss = 0.318862\n",
      "epoch 4926  Loss = 0.318852\n",
      "epoch 4927  Loss = 0.318841\n",
      "epoch 4928  Loss = 0.318831\n",
      "epoch 4929  Loss = 0.318820\n",
      "epoch 4930  Loss = 0.318810\n",
      "epoch 4931  Loss = 0.318799\n",
      "epoch 4932  Loss = 0.318789\n",
      "epoch 4933  Loss = 0.318779\n",
      "epoch 4934  Loss = 0.318768\n",
      "epoch 4935  Loss = 0.318758\n",
      "epoch 4936  Loss = 0.318747\n",
      "epoch 4937  Loss = 0.318737\n",
      "epoch 4938  Loss = 0.318726\n",
      "epoch 4939  Loss = 0.318716\n",
      "epoch 4940  Loss = 0.318705\n",
      "epoch 4941  Loss = 0.318695\n",
      "epoch 4942  Loss = 0.318685\n",
      "epoch 4943  Loss = 0.318674\n",
      "epoch 4944  Loss = 0.318664\n",
      "epoch 4945  Loss = 0.318653\n",
      "epoch 4946  Loss = 0.318643\n",
      "epoch 4947  Loss = 0.318632\n",
      "epoch 4948  Loss = 0.318622\n",
      "epoch 4949  Loss = 0.318612\n",
      "epoch 4950  Loss = 0.318601\n",
      "epoch 4951  Loss = 0.318591\n",
      "epoch 4952  Loss = 0.318580\n",
      "epoch 4953  Loss = 0.318570\n",
      "epoch 4954  Loss = 0.318559\n",
      "epoch 4955  Loss = 0.318549\n",
      "epoch 4956  Loss = 0.318539\n",
      "epoch 4957  Loss = 0.318528\n",
      "epoch 4958  Loss = 0.318518\n",
      "epoch 4959  Loss = 0.318507\n",
      "epoch 4960  Loss = 0.318497\n",
      "epoch 4961  Loss = 0.318486\n",
      "epoch 4962  Loss = 0.318476\n",
      "epoch 4963  Loss = 0.318465\n",
      "epoch 4964  Loss = 0.318455\n",
      "epoch 4965  Loss = 0.318445\n",
      "epoch 4966  Loss = 0.318434\n",
      "epoch 4967  Loss = 0.318424\n",
      "epoch 4968  Loss = 0.318413\n",
      "epoch 4969  Loss = 0.318403\n",
      "epoch 4970  Loss = 0.318392\n",
      "epoch 4971  Loss = 0.318382\n",
      "epoch 4972  Loss = 0.318372\n",
      "epoch 4973  Loss = 0.318361\n",
      "epoch 4974  Loss = 0.318351\n",
      "epoch 4975  Loss = 0.318340\n",
      "epoch 4976  Loss = 0.318330\n",
      "epoch 4977  Loss = 0.318320\n",
      "epoch 4978  Loss = 0.318309\n",
      "epoch 4979  Loss = 0.318299\n",
      "epoch 4980  Loss = 0.318288\n",
      "epoch 4981  Loss = 0.318278\n",
      "epoch 4982  Loss = 0.318267\n",
      "epoch 4983  Loss = 0.318257\n",
      "epoch 4984  Loss = 0.318247\n",
      "epoch 4985  Loss = 0.318236\n",
      "epoch 4986  Loss = 0.318226\n",
      "epoch 4987  Loss = 0.318215\n",
      "epoch 4988  Loss = 0.318205\n",
      "epoch 4989  Loss = 0.318194\n",
      "epoch 4990  Loss = 0.318184\n",
      "epoch 4991  Loss = 0.318174\n",
      "epoch 4992  Loss = 0.318163\n",
      "epoch 4993  Loss = 0.318153\n",
      "epoch 4994  Loss = 0.318142\n",
      "epoch 4995  Loss = 0.318132\n",
      "epoch 4996  Loss = 0.318122\n",
      "epoch 4997  Loss = 0.318111\n",
      "epoch 4998  Loss = 0.318101\n",
      "epoch 4999  Loss = 0.318090\n",
      "0.8686868686868687\n"
     ]
    }
   ],
   "source": [
    "import torch as t\n",
    "from torch.autograd import Variable\n",
    "from torch import nn\n",
    "submit2 = pd.read_csv(\"nn_submission.csv\")\n",
    "sequ_net = nn.Sequential(\n",
    "    nn.Linear(7, 14),  # 输入层7， 中间层14，\n",
    "    nn.Sigmoid(),  # sigmoid激活函数\n",
    "    nn.Linear(14, 1)  # 输出层为1个，即代表结果取1的概率\n",
    ")\n",
    "train_data1 = t.from_numpy(y_train).float()  # 数据转化为tensor\n",
    "Survived1 = t.from_numpy(Survived).float()\n",
    "test_data1 = t.from_numpy(y_test).float()\n",
    "parameters = sequ_net.parameters()  # 读取模块参数\n",
    "optim = t.optim.SGD(parameters, lr=1.5)  # 优化器：随机梯度下降，学习率1.5\n",
    "criterion = nn.BCEWithLogitsLoss()  # 二分类的交叉熵\n",
    "for epoch in range(5000):   # 训练5000次\n",
    "    out = sequ_net(Variable(train_data1))\n",
    "    loss = criterion(out, Variable(Survived1))  # 损失函数\n",
    "    optim.zero_grad()  # 每次梯度值清零，不然会累加\n",
    "    loss.backward()  # 反向传播\n",
    "    optim.step()  # 更新参数\n",
    "    print(\"epoch %d\" % epoch, end='  ')\n",
    "    print(\"Loss = %lf\" % loss.data.numpy())\n",
    "pre_test1 = t.sigmoid(sequ_net(Variable(test_data1))).data.numpy()  # 测试集预测\n",
    "pre_test1 = (pre_test1 > 0.5) * 1  # 大于0.5取1\n",
    "\n",
    "pre_train1 = t.sigmoid(sequ_net(Variable(train_data1))).data.numpy()  # 训练集预测\n",
    "pre_train1 = (pre_train1 > 0.5) * 1\n",
    "\n",
    "acc1 = acc = accuracy_score(Survived1, pre_train1)  # 评估精度\n",
    "print(acc1)\n",
    "submit2['Survived'] = pre_test1\n",
    "submit2.to_csv('nn_submission.csv', index=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以发现，全连接神经网络较逻辑回归精度高了不少"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.总结\n",
    "    第二个报告较第一个报告来说就难上了不少，虽然说都是多分类的问题，但解决的过程还是有不少的差别。但非常值得一提的是，在这个报告中，全连接神经网络的精度又是最高的。感觉无论是第一个报告还是这个报告，全连接神经网络都非常的好用啊。但这个报告的完成过程还是非常的令人头疼，主要还是每个特征的分析的那一块，着实是折磨人。除了一些很明显没什么影响的特征，其他的部分我都一一的进行了分析，虽然过程很累吧，但感觉还是蛮不错的诶。而且感觉就这种题目还是非常的考验人思考问题的全面性那些的。对我来说算一次很棒的经历。另外还加深了我对全连接神经网络的认识，全连接神经网络真的很强！"
   ]
  }
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